docs: Phase 1a~6 완료 체크 + API 엔드포인트 요약 표

진행 계획에 체크박스로 완료 상태 표시. Phase 7(백테스트 + shadow 예측)만 남음.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

README.md

@@ -129,15 +129,29 @@ stock_chart_site/
 
 ## 진행 계획
 
-- Phase 0 — 스캐폴드 (현재): Docker 환경 + DB 스키마 + 빈 FastAPI/Next.js + build.bat
-- Phase 1a — pykrx 데이터 파이프: 일봉/외인기관/지수 + DART + 뉴스 RSS + 거시
-- Phase 1b — KIS EOD (키 받으면)
-- Phase 2 — KR-FinBERT 감성 점수 + 일별 집계
-- Phase 3 — Chronos zero-shot 예측 적재
-- Phase 4 — LightGBM walk-forward + `prediction_outcomes` 누적 시작
-- Phase 5 — FastAPI 엔드포인트 (검색, 차트, on-demand 예측, 메트릭)
-- Phase 6 — Next.js UI (검색 + 현재 차트 + 예상차트 토글)
-- Phase 7 (옵션) — 백테스트 페이지 + 주간 자동 재학습
+- [x] Phase 0 — 스캐폴드: Docker 환경 + DB 스키마 + FastAPI/Next.js + build.bat
+- [x] Phase 1a — pykrx 데이터 파이프: 일봉/외인기관/지수 + DART + 뉴스 RSS + 거시
+- [x] Phase 1b — KIS read-only EOD (스모크 통과)
+- [x] Phase 2 — KR-FinBERT 감성 점수 + 일별 집계 뷰
+- [x] Phase 3 — Chronos zero-shot 예측 어댑터 + 피처 빌더
+- [x] Phase 4 — LightGBM walk-forward + ensemble + 매칭/재학습 잡
+- [x] Phase 5 — FastAPI 엔드포인트 (검색/차트/예측/메트릭/뉴스)
+- [x] Phase 6 — Next.js UI (검색 + 현재 차트 + 예상차트 overlay)
+- [ ] Phase 7 (옵션) — 백테스트 페이지 + Chronos/LGBM 단독 shadow 예측
+
+### API 엔드포인트 (요약)
+
+| 메서드 | 경로 | 설명 |
+|---|---|---|
+| GET | `/health`, `/health/db`, `/health/keys` | 헬스/외부키 ping |
+| POST | `/api/refresh/{code}?lookback_days=N` | 수동 갱신 |
+| GET | `/api/symbols/search?q=&seed_only=` | 종목 검색 (trigram + prefix) |
+| GET | `/api/symbols/{code}` | 종목 메타 |
+| GET | `/api/chart/{code}?days=N` | OHLCV + 감성 + 외인기관거래대금 |
+| POST | `/api/predict/{code}?horizons=1,3,5` | on-demand 앙상블 예측 (user_triggered) |
+| GET | `/api/predict/{code}/latest` | 최신 base_date 예측 묶음 (UI overlay) |
+| GET | `/api/metrics/{code}?window_days=N` | 종목 hit_rate / mae |
+| GET | `/api/news/{code}?limit=N&source=` | 최근 뉴스/공시 + 감성 |
 
 ## 동작 모델 메모
 

backend/app/api/chart.py

@@ -0,0 +1,116 @@
+"""차트 데이터 API: OHLCV + 보조 데이터 (감성, 거시).
+
+UI: /code 페이지 첫 로드 시 호출 → lightweight-charts 캔들 데이터로 사용.
+"""
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+router = APIRouter(prefix="/api/chart", tags=["chart"])
+
+
+@router.get("/{code}")
+def get_chart(
+    code: str,
+    days: int = Query(default=180, ge=10, le=3650),
+    include_sentiment: bool = Query(default=True),
+    include_trading_value: bool = Query(default=True),
+) -> dict:
+    eng = get_engine()
+    end = date.today()
+    start = end - timedelta(days=days)
+    with eng.connect() as conn:
+        symbol = conn.execute(
+            text("SELECT code, name, market FROM symbols WHERE code = :c"),
+            {"c": code},
+        ).first()
+        if not symbol:
+            raise HTTPException(status_code=404, detail=f"unknown code: {code}")
+
+        ohlcv_rows = conn.execute(
+            text(
+                """
+                SELECT date, open, high, low, close, volume
+                  FROM ohlcv_daily
+                 WHERE code = :c AND date BETWEEN :s AND :e
+                 ORDER BY date
+                """
+            ),
+            {"c": code, "s": start, "e": end},
+        ).all()
+        ohlcv = [
+            {
+                "date": str(r[0]),
+                "open": float(r[1]) if r[1] is not None else None,
+                "high": float(r[2]) if r[2] is not None else None,
+                "low": float(r[3]) if r[3] is not None else None,
+                "close": float(r[4]) if r[4] is not None else None,
+                "volume": int(r[5]) if r[5] is not None else None,
+            }
+            for r in ohlcv_rows
+        ]
+
+        sentiment: list[dict] = []
+        if include_sentiment:
+            try:
+                s_rows = conn.execute(
+                    text(
+                        """
+                        SELECT date, n_articles, mean_score, weighted_score
+                          FROM v_sentiment_daily
+                         WHERE code = :c AND date BETWEEN :s AND :e
+                         ORDER BY date
+                        """
+                    ),
+                    {"c": code, "s": start, "e": end},
+                ).all()
+                sentiment = [
+                    {
+                        "date": str(r[0]),
+                        "n_articles": int(r[1]) if r[1] is not None else 0,
+                        "mean_score": float(r[2]) if r[2] is not None else None,
+                        "weighted_score": float(r[3]) if r[3] is not None else None,
+                    }
+                    for r in s_rows
+                ]
+            except Exception:  # noqa: BLE001
+                # v_sentiment_daily 뷰 아직 없을 수 있음 (마이그레이션 미실행)
+                sentiment = []
+
+        trading: list[dict] = []
+        if include_trading_value:
+            tv_rows = conn.execute(
+                text(
+                    """
+                    SELECT date, foreign_net, institution_net, individual_net
+                      FROM trading_value_daily
+                     WHERE code = :c AND date BETWEEN :s AND :e
+                     ORDER BY date
+                    """
+                ),
+                {"c": code, "s": start, "e": end},
+            ).all()
+            trading = [
+                {
+                    "date": str(r[0]),
+                    "foreign_net": float(r[1]) if r[1] is not None else None,
+                    "institution_net": float(r[2]) if r[2] is not None else None,
+                    "individual_net": float(r[3]) if r[3] is not None else None,
+                }
+                for r in tv_rows
+            ]
+
+    return {
+        "code": symbol[0],
+        "name": symbol[1],
+        "market": symbol[2],
+        "range": {"from": str(start), "to": str(end)},
+        "ohlcv": ohlcv,
+        "sentiment": sentiment,
+        "trading_value": trading,
+    }

backend/app/api/metrics.py

@@ -0,0 +1,101 @@
+"""모델 성능 메트릭 API."""
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+router = APIRouter(prefix="/api/metrics", tags=["metrics"])
+
+
+@router.get("/{code}")
+def code_metrics(
+    code: str,
+    window_days: int = Query(default=30, ge=1, le=365),
+) -> dict:
+    """code 의 최근 window_days 윈도우 prediction_outcomes 집계."""
+    eng = get_engine()
+    end = date.today()
+    start = end - timedelta(days=window_days)
+    with eng.connect() as conn:
+        sym = conn.execute(
+            text("SELECT code, name FROM symbols WHERE code = :c"),
+            {"c": code},
+        ).first()
+        if not sym:
+            raise HTTPException(status_code=404, detail=f"unknown code: {code}")
+        rows = conn.execute(
+            text(
+                """
+                SELECT model, horizon,
+                       COUNT(*) AS n,
+                       AVG(CASE WHEN direction_hit THEN 1.0 ELSE 0.0 END) AS hit_rate,
+                       AVG(abs_error) AS mae
+                  FROM prediction_outcomes
+                 WHERE code = :c AND resolved_at >= :s
+                 GROUP BY model, horizon
+                 ORDER BY model, horizon
+                """
+            ),
+            {"c": code, "s": start},
+        ).all()
+
+    return {
+        "code": sym[0],
+        "name": sym[1],
+        "window_days": window_days,
+        "range": {"from": str(start), "to": str(end)},
+        "by_model_horizon": [
+            {
+                "model": r[0],
+                "horizon": int(r[1]),
+                "n": int(r[2]),
+                "hit_rate": float(r[3]) if r[3] is not None else None,
+                "mae": float(r[4]) if r[4] is not None else None,
+            }
+            for r in rows
+        ],
+    }
+
+
+@router.get("")
+def overall_metrics(
+    window_days: int = Query(default=30, ge=1, le=365),
+) -> dict:
+    """전체 시드 종목 누적 메트릭."""
+    eng = get_engine()
+    end = date.today()
+    start = end - timedelta(days=window_days)
+    with eng.connect() as conn:
+        rows = conn.execute(
+            text(
+                """
+                SELECT po.model, po.horizon,
+                       COUNT(*) AS n,
+                       AVG(CASE WHEN po.direction_hit THEN 1.0 ELSE 0.0 END) AS hit_rate,
+                       AVG(po.abs_error) AS mae
+                  FROM prediction_outcomes po
+                 WHERE po.resolved_at >= :s
+                 GROUP BY po.model, po.horizon
+                 ORDER BY po.model, po.horizon
+                """
+            ),
+            {"s": start},
+        ).all()
+    return {
+        "window_days": window_days,
+        "range": {"from": str(start), "to": str(end)},
+        "by_model_horizon": [
+            {
+                "model": r[0],
+                "horizon": int(r[1]),
+                "n": int(r[2]),
+                "hit_rate": float(r[3]) if r[3] is not None else None,
+                "mae": float(r[4]) if r[4] is not None else None,
+            }
+            for r in rows
+        ],
+    }

backend/app/api/news.py

@@ -0,0 +1,58 @@
+"""뉴스/공시 목록 API."""
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+router = APIRouter(prefix="/api/news", tags=["news"])
+
+
+@router.get("/{code}")
+def list_news(
+    code: str,
+    limit: int = Query(default=20, ge=1, le=200),
+    source: str | None = Query(default=None, description="naver_finance / google_rss / dart"),
+) -> dict:
+    eng = get_engine()
+    with eng.connect() as conn:
+        sym = conn.execute(
+            text("SELECT code, name FROM symbols WHERE code = :c"),
+            {"c": code},
+        ).first()
+        if not sym:
+            raise HTTPException(status_code=404, detail=f"unknown code: {code}")
+        where = "code = :c"
+        params: dict = {"c": code, "lim": limit}
+        if source:
+            where += " AND source = :src"
+            params["src"] = source
+        rows = conn.execute(
+            text(
+                f"""
+                SELECT source, published_at, title, url, sentiment_score, sentiment_label
+                  FROM news
+                 WHERE {where}
+                 ORDER BY published_at DESC
+                 LIMIT :lim
+                """
+            ),
+            params,
+        ).all()
+    return {
+        "code": sym[0],
+        "name": sym[1],
+        "count": len(rows),
+        "items": [
+            {
+                "source": r[0],
+                "published_at": r[1].isoformat() if r[1] else None,
+                "title": r[2],
+                "url": r[3],
+                "sentiment_score": float(r[4]) if r[4] is not None else None,
+                "sentiment_label": r[5],
+            }
+            for r in rows
+        ],
+    }

backend/app/api/predict.py

@@ -0,0 +1,137 @@
+"""예측 API.
+
+POST /api/predict/{code}                : on-demand 예측 + predictions 적재 (user_triggered=TRUE)
+GET  /api/predict/{code}/latest         : 가장 최근 base_date 의 예측 horizons 묶음 반환
+                                           (UI 가 새로고침해도 같은 결과 보여줄 때 사용)
+"""
+from __future__ import annotations
+
+import json
+import logging
+from datetime import datetime, timedelta, timezone
+
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+from app.pipelines.predict_one import predict_and_store
+
+logger = logging.getLogger(__name__)
+KST = timezone(timedelta(hours=9))
+
+router = APIRouter(prefix="/api/predict", tags=["predict"])
+
+
+@router.post("/{code}")
+def predict_endpoint(
+    code: str,
+    horizons: str = Query(default="1,3,5", description="쉼표 구분 정수. ex) '1,3,5'"),
+    user_triggered: bool = Query(default=True),
+) -> dict:
+    """on-demand 예측. UI 의 '예상차트 보기' 클릭이 호출."""
+    eng = get_engine()
+    with eng.connect() as conn:
+        row = conn.execute(
+            text("SELECT code, name FROM symbols WHERE code = :c"),
+            {"c": code},
+        ).first()
+    if not row:
+        raise HTTPException(status_code=404, detail=f"unknown code: {code}")
+
+    try:
+        hs = tuple(int(x) for x in horizons.split(",") if x.strip())
+        if not hs or any(h < 1 or h > 30 for h in hs):
+            raise ValueError("invalid horizons")
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=f"bad horizons: {exc}")
+
+    try:
+        result = predict_and_store(code, horizons=hs, user_triggered=user_triggered)
+    except RuntimeError as exc:
+        raise HTTPException(status_code=409, detail=str(exc))
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("predict_and_store failed for %s", code)
+        raise HTTPException(status_code=500, detail=str(exc))
+
+    return result
+
+
+@router.get("/{code}/latest")
+def latest_prediction(code: str) -> dict:
+    """가장 최근 base_date 에 대한 'ensemble' 예측 묶음."""
+    eng = get_engine()
+    with eng.connect() as conn:
+        symbol = conn.execute(
+            text("SELECT code, name FROM symbols WHERE code = :c"),
+            {"c": code},
+        ).first()
+        if not symbol:
+            raise HTTPException(status_code=404, detail=f"unknown code: {code}")
+
+        latest_base = conn.execute(
+            text(
+                "SELECT MAX(base_date) FROM predictions "
+                "WHERE code = :c AND model = 'ensemble'"
+            ),
+            {"c": code},
+        ).first()
+        if not latest_base or latest_base[0] is None:
+            return {"code": code, "found": False, "steps": []}
+        base_date = latest_base[0]
+
+        rows = conn.execute(
+            text(
+                """
+                SELECT predicted_at, target_date, horizon, direction,
+                       prob_up, prob_flat, prob_down, expected_return,
+                       point_forecast, ci_low, ci_high, user_triggered,
+                       features_snapshot
+                  FROM predictions
+                 WHERE code = :c AND base_date = :bd AND model = 'ensemble'
+                 ORDER BY horizon
+                """
+            ),
+            {"c": code, "bd": base_date},
+        ).all()
+
+        # base_close (그 날 ohlcv) 도 같이 반환 — UI 가 차트 마지막 점에 이어붙일 때 사용
+        base_close_row = conn.execute(
+            text("SELECT close FROM ohlcv_daily WHERE code = :c AND date = :d"),
+            {"c": code, "d": base_date},
+        ).first()
+        base_close = float(base_close_row[0]) if base_close_row and base_close_row[0] is not None else None
+
+    steps = []
+    for r in rows:
+        feats = r[12]
+        if isinstance(feats, str):
+            try:
+                feats = json.loads(feats)
+            except Exception:  # noqa: BLE001
+                pass
+        steps.append(
+            {
+                "predicted_at": r[0].isoformat() if r[0] else None,
+                "target_date": str(r[1]),
+                "horizon": int(r[2]),
+                "direction": r[3],
+                "prob_up": float(r[4]) if r[4] is not None else None,
+                "prob_flat": float(r[5]) if r[5] is not None else None,
+                "prob_down": float(r[6]) if r[6] is not None else None,
+                "expected_return": float(r[7]) if r[7] is not None else None,
+                "point_close": float(r[8]) if r[8] is not None else None,
+                "ci_low": float(r[9]) if r[9] is not None else None,
+                "ci_high": float(r[10]) if r[10] is not None else None,
+                "user_triggered": bool(r[11]),
+                "features_snapshot": feats,
+            }
+        )
+
+    return {
+        "code": symbol[0],
+        "name": symbol[1],
+        "found": True,
+        "base_date": str(base_date),
+        "base_close": base_close,
+        "steps": steps,
+    }

backend/app/api/symbols.py

@@ -0,0 +1,101 @@
+"""종목 검색 / 메타 API."""
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+router = APIRouter(prefix="/api/symbols", tags=["symbols"])
+
+
+@router.get("/search")
+def search_symbols(
+    q: str = Query(..., min_length=1, max_length=40, description="종목명 또는 코드 prefix/부분 일치"),
+    limit: int = Query(default=20, ge=1, le=100),
+    seed_only: bool = Query(default=False, description="true 면 학습/배치 대상 10종목만"),
+) -> dict:
+    """이름은 trigram + ILIKE, 코드는 prefix 매치.
+
+    우선순위:
+      1) 코드가 정확히 같으면 가장 위
+      2) 이름 prefix 매치
+      3) 이름 부분 매치 (trigram similarity)
+    """
+    q_norm = q.strip()
+    if not q_norm:
+        raise HTTPException(status_code=400, detail="empty query")
+
+    eng = get_engine()
+    where_seed = "AND is_seed = TRUE" if seed_only else ""
+    sql = text(
+        f"""
+        WITH ranked AS (
+          SELECT code, name, market, sector, is_seed,
+            CASE
+              WHEN code = :q                          THEN 0
+              WHEN code LIKE :prefix                  THEN 1
+              WHEN name LIKE :prefix                  THEN 2
+              WHEN name ILIKE :contains               THEN 3
+              ELSE 4
+            END AS rank,
+            similarity(name, :q) AS sim
+            FROM symbols
+           WHERE active = TRUE
+             {where_seed}
+             AND (code LIKE :prefix OR name ILIKE :contains OR similarity(name, :q) > 0.2)
+        )
+        SELECT code, name, market, sector, is_seed
+          FROM ranked
+         ORDER BY rank ASC, sim DESC, name ASC
+         LIMIT :lim
+        """
+    )
+    with eng.connect() as conn:
+        rows = conn.execute(
+            sql,
+            {
+                "q": q_norm,
+                "prefix": f"{q_norm}%",
+                "contains": f"%{q_norm}%",
+                "lim": limit,
+            },
+        ).all()
+    return {
+        "q": q_norm,
+        "count": len(rows),
+        "items": [
+            {
+                "code": r[0],
+                "name": r[1],
+                "market": r[2],
+                "sector": r[3],
+                "is_seed": bool(r[4]),
+            }
+            for r in rows
+        ],
+    }
+
+
+@router.get("/{code}")
+def get_symbol(code: str) -> dict:
+    eng = get_engine()
+    with eng.connect() as conn:
+        row = conn.execute(
+            text(
+                "SELECT code, name, market, sector, is_seed, active, created_at "
+                "FROM symbols WHERE code = :c"
+            ),
+            {"c": code},
+        ).first()
+    if not row:
+        raise HTTPException(status_code=404, detail=f"unknown code: {code}")
+    return {
+        "code": row[0],
+        "name": row[1],
+        "market": row[2],
+        "sector": row[3],
+        "is_seed": bool(row[4]),
+        "active": bool(row[5]),
+        "created_at": str(row[6]) if row[6] else None,
+    }

backend/app/db/migrate.py

@@ -0,0 +1,53 @@
+"""Manual migration runner.
+
+docker-entrypoint-initdb.d 는 fresh DB 첫 기동 때만 동작. 이미 동작 중인 DB 에
+새 마이그레이션을 적용하려면 이 스크립트로:
+
+    python -m app.db.migrate
+
+모든 SQL 파일은 idempotent (CREATE IF NOT EXISTS / CREATE OR REPLACE) 여야 함.
+"""
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+logger = logging.getLogger(__name__)
+
+MIGRATIONS_DIR = Path(__file__).parent / "migrations"
+
+
+def apply_all() -> dict[str, str]:
+    """migrations/ 안 .sql 들을 이름순으로 적용. 결과: {filename: 'ok'|'failed: ...'}."""
+    eng = get_engine()
+    results: dict[str, str] = {}
+    files = sorted(MIGRATIONS_DIR.glob("*.sql"))
+    if not files:
+        logger.warning("no migration files in %s", MIGRATIONS_DIR)
+        return results
+    for path in files:
+        sql = path.read_text(encoding="utf-8")
+        # psql meta-command 제거 (\set ON_ERROR_STOP 등)
+        cleaned = "\n".join(
+            ln for ln in sql.splitlines() if not ln.strip().startswith("\\")
+        )
+        try:
+            with eng.begin() as conn:
+                conn.execute(text(cleaned))
+            results[path.name] = "ok"
+            logger.info("applied %s", path.name)
+        except Exception as exc:  # noqa: BLE001
+            results[path.name] = f"failed: {exc}"
+            logger.exception("migration %s failed", path.name)
+    return results
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    out = apply_all()
+    for k, v in out.items():
+        print(f"{k}: {v}")

backend/app/db/migrations/002_sentiment_view.sql

@@ -0,0 +1,32 @@
+-- Phase 2: 일별 종목별 감성 집계 뷰.
+-- weighted_score : 소스별 가중치 적용
+--   naver_finance 1.0 (가장 직접적인 종목 페이지 뉴스)
+--   google_rss    0.7 (관련성 노이즈 있음)
+--   dart          0.5 (공시는 short title 만으로는 감성이 약함)
+
+\set ON_ERROR_STOP on
+
+CREATE OR REPLACE VIEW v_sentiment_daily AS
+SELECT
+  code,
+  (published_at AT TIME ZONE 'Asia/Seoul')::date                AS date,
+  COUNT(*)                                                       AS n_articles,
+  AVG(sentiment_score)::REAL                                     AS mean_score,
+  AVG(CASE WHEN sentiment_label = 'positive' THEN 1.0 ELSE 0.0 END)::REAL AS pos_ratio,
+  AVG(CASE WHEN sentiment_label = 'negative' THEN 1.0 ELSE 0.0 END)::REAL AS neg_ratio,
+  AVG(CASE WHEN sentiment_label = 'neutral'  THEN 1.0 ELSE 0.0 END)::REAL AS neu_ratio,
+  AVG(
+    sentiment_score * CASE source
+      WHEN 'naver_finance' THEN 1.0
+      WHEN 'google_rss'    THEN 0.7
+      WHEN 'dart'          THEN 0.5
+      ELSE 0.6
+    END
+  )::REAL                                                        AS weighted_score
+FROM news
+WHERE sentiment_score IS NOT NULL
+  AND code IS NOT NULL
+GROUP BY code, (published_at AT TIME ZONE 'Asia/Seoul')::date;
+
+COMMENT ON VIEW v_sentiment_daily IS
+  'Phase 2: KR-FinBERT 점수를 종목·일(KST) 단위로 집계. Phase 4 LGBM 피처 + UI 차트 보조 데이터로 사용.';

backend/app/db/migrations/003_ensemble_weights.sql

@@ -0,0 +1,19 @@
+-- Phase 4: 앙상블 가중치 저장.
+-- (code, horizon) 별로 Chronos vs LGBM 가중치. 일요일 02:00 재학습 잡에서 갱신.
+
+\set ON_ERROR_STOP on
+
+CREATE TABLE IF NOT EXISTS ensemble_weights (
+  code        TEXT NOT NULL REFERENCES symbols(code),
+  horizon     INT NOT NULL,
+  w_chronos   REAL NOT NULL DEFAULT 0.6,
+  w_lgbm      REAL NOT NULL DEFAULT 0.4,
+  hit_rate_chronos REAL,
+  hit_rate_lgbm    REAL,
+  sample_count     INT,
+  updated_at  TIMESTAMPTZ DEFAULT NOW(),
+  PRIMARY KEY (code, horizon)
+);
+
+COMMENT ON TABLE ensemble_weights IS
+  'Phase 4: (code, horizon) 별 Chronos/LGBM 가중치. 최근 30일 prediction_outcomes hit_rate 기반 매주 갱신.';

backend/app/main.py

@@ -6,7 +6,12 @@ from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
+from app.api.chart import router as chart_router
+from app.api.metrics import router as metrics_router
+from app.api.news import router as news_router
+from app.api.predict import router as predict_router
 from app.api.refresh import router as refresh_router
+from app.api.symbols import router as symbols_router
 from app.config import settings
 from app.db.connection import ping as db_ping
 from app.fetch import dart as dart_mod
@@ -41,6 +46,11 @@ app.add_middleware(
 )
 
 app.include_router(refresh_router)
+app.include_router(symbols_router)
+app.include_router(chart_router)
+app.include_router(predict_router)
+app.include_router(metrics_router)
+app.include_router(news_router)
 
 
 def _resolved_device() -> str:

backend/app/models/chronos.py

@@ -0,0 +1,118 @@
+"""Chronos zero-shot 시계열 예측 어댑터.
+
+모델: amazon/chronos-t5-small (46M, 빠르고 RTX 3070 Ti 에 충분히 들어감).
+환경변수 CHRONOS_MODEL 로 base/large 로 바꿀 수 있음.
+
+입력: 종가 시계열 (list[float], 최소 32 step).
+출력: horizon 일 quantile forecast (q10/median/q90).
+
+lazy singleton 으로 첫 호출 시 모델 로드. 디바이스는 settings.model_device 따라.
+"""
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from dataclasses import dataclass
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+MODEL_NAME = os.environ.get("CHRONOS_MODEL", "amazon/chronos-t5-small")
+
+_lock = threading.Lock()
+_state: dict[str, object] = {"loaded": False, "pipe": None, "device": None}
+
+
+@dataclass
+class ChronosForecast:
+    horizon: int
+    median: list[float]
+    q10: list[float]
+    q90: list[float]
+    samples: list[list[float]]  # raw samples for ensemble downstream
+
+
+def _resolve_device() -> str:
+    import torch  # lazy
+
+    pref = (settings.model_device or "auto").lower()
+    if pref == "cuda":
+        return "cuda" if torch.cuda.is_available() else "cpu"
+    if pref == "cpu":
+        return "cpu"
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def _load() -> None:
+    global _state
+    with _lock:
+        if _state["loaded"]:
+            return
+        import torch
+        from chronos import ChronosPipeline
+
+        token = settings.huggingface_token or None
+        if token:
+            os.environ.setdefault("HUGGINGFACE_HUB_TOKEN", token)
+            os.environ.setdefault("HF_TOKEN", token)
+
+        device = _resolve_device()
+        # bf16 은 RTX 30xx 이상에서 지원. cpu 에선 fp32.
+        dtype = torch.bfloat16 if device == "cuda" else torch.float32
+        logger.info("loading Chronos %s on %s (dtype=%s)", MODEL_NAME, device, dtype)
+        pipe = ChronosPipeline.from_pretrained(
+            MODEL_NAME,
+            device_map=device,
+            torch_dtype=dtype,
+        )
+        _state.update({"loaded": True, "pipe": pipe, "device": device})
+
+
+def forecast(
+    series: list[float],
+    *,
+    horizon: int = 5,
+    num_samples: int = 30,
+) -> ChronosForecast:
+    """series 의 마지막 시점 이후 horizon 일 예측.
+
+    series 는 일봉 종가. 최소 32개 권장 (그보다 짧으면 Chronos 분위 안정성 떨어짐).
+    """
+    if len(series) < 32:
+        raise ValueError(
+            f"series too short ({len(series)}) for Chronos forecast (need >=32)"
+        )
+    _load()
+    import numpy as np
+    import torch
+
+    pipe = _state["pipe"]
+    context = torch.tensor([float(x) for x in series], dtype=torch.float32)
+    with torch.no_grad():
+        samples = pipe.predict(
+            context=context,
+            prediction_length=horizon,
+            num_samples=num_samples,
+        )
+    # samples: (1, num_samples, prediction_length)
+    arr = samples[0].cpu().float().numpy()
+    q10 = np.quantile(arr, 0.10, axis=0).tolist()
+    median = np.quantile(arr, 0.50, axis=0).tolist()
+    q90 = np.quantile(arr, 0.90, axis=0).tolist()
+    return ChronosForecast(
+        horizon=horizon,
+        median=[float(x) for x in median],
+        q10=[float(x) for x in q10],
+        q90=[float(x) for x in q90],
+        samples=[[float(x) for x in row] for row in arr.tolist()],
+    )
+
+
+def ping() -> dict[str, object]:
+    try:
+        _load()
+        return {"status": "ok", "model": MODEL_NAME, "device": _state["device"]}
+    except Exception as exc:  # noqa: BLE001
+        return {"status": "failed", "model": MODEL_NAME, "error": str(exc)}

backend/app/models/ensemble.py

@@ -0,0 +1,174 @@
+"""Chronos + LGBM 앙상블 추론.
+
+  final_price[h]   = w_c * chronos.median[h-1] + w_l * lgbm.predicted_close
+  final_q10[h]     = w_c * chronos.q10[h-1]    + w_l * lgbm.predicted_close * 0.97
+  final_q90[h]     = w_c * chronos.q90[h-1]    + w_l * lgbm.predicted_close * 1.03
+
+LGBM 은 단일 horizon 의 다음 종가(point) 만 주므로, 그 자체로는 신뢰구간이 없음.
+근사로 ±3% band 를 LGBM 의 q10/q90 자리에 사용. Chronos 의 sample 분포가
+주된 신뢰구간 정보 (Chronos 우세하면 ci 가 좁아짐).
+
+direction 확률:
+- LGBM 분류기에서 prob_up/flat/down (3-class) 그대로
+- Chronos 는 next-day return 부호 비율: samples.shift1 / base_close - 1 의 부호
+- 둘을 같은 가중치로 평균
+
+LGBM 모델이 없으면 Chronos 단독으로 진행 (cold start).
+Chronos 도 실패하면 LGBM 단독으로 진행.
+"""
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+
+import numpy as np
+
+from app.models.chronos import ChronosForecast
+from app.models.chronos import forecast as chronos_forecast
+from app.models.lgbm import LgbmForecast
+from app.models.lgbm import predict_one as lgbm_predict
+from app.models.weights import load_weights
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EnsembleStep:
+    horizon: int                  # 1..H 거래일 후
+    target_idx: int               # chronos median 의 0-based 인덱스 (horizon-1)
+    point_close: float
+    ci_low: float
+    ci_high: float
+    prob_up: float
+    prob_flat: float
+    prob_down: float
+    direction: str                # 'up' / 'flat' / 'down'
+    expected_return: float
+
+
+@dataclass
+class EnsemblePrediction:
+    code: str
+    base_close: float
+    horizons: list[int]
+    steps: list[EnsembleStep]
+    sources_used: list[str]
+
+
+def _chronos_direction(samples: list[list[float]], base_close: float, horizon: int) -> tuple[float, float, float]:
+    """Chronos sample 분포에서 (prob_up, prob_flat, prob_down). ±0.3% flat band."""
+    if not samples:
+        return 0.33, 0.34, 0.33
+    arr = np.array(samples)[:, horizon - 1]  # 해당 step 의 sample 값
+    ret = arr / base_close - 1.0
+    p_up = float((ret > 0.003).mean())
+    p_dn = float((ret < -0.003).mean())
+    p_fl = 1.0 - p_up - p_dn
+    return p_up, p_fl, p_dn
+
+
+def predict(code: str, *, horizons: tuple[int, ...] = (1, 3, 5)) -> EnsemblePrediction:
+    """한 종목에 대해 horizons 별 앙상블 예측. on-demand 추론용."""
+    max_h = max(horizons)
+
+    # Chronos: 종가 시계열 가져와서 max_h 까지 예측.
+    from app.models.features import build_features  # local import
+
+    ff = build_features(code, lookback_days=400, horizons=horizons, with_targets=False)
+    df = ff.df
+    if df.empty:
+        raise RuntimeError(f"no OHLCV data for {code}")
+    closes = df["close"].astype(float).tolist()
+    base_close = float(closes[-1])
+
+    sources_used: list[str] = []
+    cf: ChronosForecast | None = None
+    try:
+        cf = chronos_forecast(closes, horizon=max_h, num_samples=30)
+        sources_used.append("chronos")
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("chronos forecast failed for %s: %s", code, exc)
+
+    steps: list[EnsembleStep] = []
+    for h in horizons:
+        lf: LgbmForecast | None = None
+        try:
+            lf = lgbm_predict(code, h)
+            if lf is not None:
+                sources_used.append(f"lgbm_h{h}")
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("lgbm predict failed for %s h=%d: %s", code, h, exc)
+
+        # 가중치 (DB 없으면 default 0.6/0.4).
+        w = load_weights(code, h)
+        wc, wl = w.w_chronos, w.w_lgbm
+        # 한쪽이 없으면 다른 쪽 전부.
+        if cf is None and lf is None:
+            raise RuntimeError(f"both chronos & lgbm failed for {code} h={h}")
+        if cf is None:
+            wc, wl = 0.0, 1.0
+        if lf is None:
+            wc, wl = 1.0, 0.0
+
+        if cf is not None:
+            c_med = cf.median[h - 1]
+            c_q10 = cf.q10[h - 1]
+            c_q90 = cf.q90[h - 1]
+        else:
+            c_med = c_q10 = c_q90 = base_close  # not used (wc=0)
+
+        if lf is not None:
+            l_close = lf.predicted_close
+            l_lo = l_close * 0.97
+            l_hi = l_close * 1.03
+            l_pu, l_pf, l_pd = lf.prob_up, lf.prob_flat, lf.prob_down
+        else:
+            l_close = l_lo = l_hi = base_close
+            l_pu = l_pf = l_pd = 0.0
+
+        point = wc * c_med + wl * l_close
+        lo = wc * c_q10 + wl * l_lo
+        hi = wc * c_q90 + wl * l_hi
+
+        if cf is not None:
+            cp_up, cp_fl, cp_dn = _chronos_direction(cf.samples, base_close, h)
+        else:
+            cp_up = cp_fl = cp_dn = 0.0
+
+        # direction prob: source 마다 weights 동일하게 가중평균
+        if lf is not None and cf is not None:
+            p_up = 0.5 * cp_up + 0.5 * l_pu
+            p_fl = 0.5 * cp_fl + 0.5 * l_pf
+            p_dn = 0.5 * cp_dn + 0.5 * l_pd
+        elif cf is not None:
+            p_up, p_fl, p_dn = cp_up, cp_fl, cp_dn
+        else:
+            p_up, p_fl, p_dn = l_pu, l_pf, l_pd
+
+        # 정규화 (혹시 합이 0 가 아닐 때)
+        s = max(p_up + p_fl + p_dn, 1e-9)
+        p_up, p_fl, p_dn = p_up / s, p_fl / s, p_dn / s
+        dir_lbl = "up" if p_up >= max(p_fl, p_dn) else ("down" if p_dn >= p_fl else "flat")
+
+        steps.append(
+            EnsembleStep(
+                horizon=h,
+                target_idx=h - 1,
+                point_close=float(point),
+                ci_low=float(lo),
+                ci_high=float(hi),
+                prob_up=float(p_up),
+                prob_flat=float(p_fl),
+                prob_down=float(p_dn),
+                direction=dir_lbl,
+                expected_return=float(point / base_close - 1.0),
+            )
+        )
+
+    return EnsemblePrediction(
+        code=code,
+        base_close=base_close,
+        horizons=list(horizons),
+        steps=steps,
+        sources_used=sources_used,
+    )

backend/app/models/features.py

@@ -0,0 +1,223 @@
+"""모델 학습/추론용 피처 빌더.
+
+종목 1개 + 룩백 기간을 받아 (date 단위) DataFrame 반환:
+  - OHLCV
+  - returns r1
+  - TA: rsi14, macd, macd_signal, atr14, bb_pct, sma20, ema12, vol_z20
+  - trading_value: foreign_net, institution_net, individual_net (정규화 X, scale 그대로)
+  - macro 정렬: kospi, kosdaq, usdkrw, us10y, kospi_r1, usdkrw_r1
+  - sentiment (v_sentiment_daily): mean_score, weighted_score, n_articles,
+    pos_minus_neg = pos_ratio - neg_ratio. 3일 롤링 mean 도 추가.
+
+학습 타깃 (build_features 에서만 생성):
+  - y_close_h{1,3,5}: close.shift(-H)
+  - y_ret_h{1,3,5}:   y_close_h / close - 1
+  - y_dir_h{1,3,5}:   sign(y_ret_h) (1=up, -1=down, 0=flat ±0.3% 이내)
+
+inference 용 build_features 는 dropna 안 함. 학습용 build_training_frame 은 dropna.
+"""
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from datetime import date, timedelta
+
+import numpy as np
+import pandas as pd
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+logger = logging.getLogger(__name__)
+
+FLAT_BAND = 0.003  # ±0.3% 이내는 flat
+HORIZONS_DEFAULT = (1, 3, 5)
+
+
+@dataclass
+class FeatureFrame:
+    code: str
+    df: pd.DataFrame
+    target_horizons: tuple[int, ...]
+
+
+def _load_ohlcv(code: str, start: date, end: date) -> pd.DataFrame:
+    eng = get_engine()
+    sql = text(
+        """
+        SELECT date, open, high, low, close, volume
+          FROM ohlcv_daily
+         WHERE code = :code AND date BETWEEN :s AND :e
+         ORDER BY date
+        """
+    )
+    with eng.connect() as conn:
+        rows = conn.execute(sql, {"code": code, "s": start, "e": end}).all()
+    if not rows:
+        return pd.DataFrame(columns=["date", "open", "high", "low", "close", "volume"])
+    df = pd.DataFrame(rows, columns=["date", "open", "high", "low", "close", "volume"])
+    df["date"] = pd.to_datetime(df["date"]).dt.date
+    return df
+
+
+def _load_trading(code: str, start: date, end: date) -> pd.DataFrame:
+    eng = get_engine()
+    sql = text(
+        """
+        SELECT date, foreign_net, institution_net, individual_net
+          FROM trading_value_daily
+         WHERE code = :code AND date BETWEEN :s AND :e
+         ORDER BY date
+        """
+    )
+    with eng.connect() as conn:
+        rows = conn.execute(sql, {"code": code, "s": start, "e": end}).all()
+    if not rows:
+        return pd.DataFrame(columns=["date", "foreign_net", "institution_net", "individual_net"])
+    df = pd.DataFrame(rows, columns=["date", "foreign_net", "institution_net", "individual_net"])
+    df["date"] = pd.to_datetime(df["date"]).dt.date
+    return df
+
+
+def _load_macro(start: date, end: date) -> pd.DataFrame:
+    eng = get_engine()
+    sql = text(
+        "SELECT date, key, value FROM macro_daily "
+        "WHERE date BETWEEN :s AND :e ORDER BY date"
+    )
+    with eng.connect() as conn:
+        rows = conn.execute(sql, {"s": start, "e": end}).all()
+    if not rows:
+        return pd.DataFrame(columns=["date"])
+    df = pd.DataFrame(rows, columns=["date", "key", "value"])
+    pivot = df.pivot_table(index="date", columns="key", values="value", aggfunc="last").reset_index()
+    pivot["date"] = pd.to_datetime(pivot["date"]).dt.date
+    pivot.columns.name = None
+    return pivot
+
+
+def _load_sentiment(code: str, start: date, end: date) -> pd.DataFrame:
+    eng = get_engine()
+    sql = text(
+        """
+        SELECT date, n_articles, mean_score, pos_ratio, neg_ratio,
+               weighted_score
+          FROM v_sentiment_daily
+         WHERE code = :code AND date BETWEEN :s AND :e
+         ORDER BY date
+        """
+    )
+    with eng.connect() as conn:
+        rows = conn.execute(sql, {"code": code, "s": start, "e": end}).all()
+    cols = ["date", "n_articles", "mean_score", "pos_ratio", "neg_ratio", "weighted_score"]
+    if not rows:
+        return pd.DataFrame(columns=cols)
+    df = pd.DataFrame(rows, columns=cols)
+    df["date"] = pd.to_datetime(df["date"]).dt.date
+    df["pos_minus_neg"] = df["pos_ratio"].fillna(0) - df["neg_ratio"].fillna(0)
+    return df
+
+
+def _add_ta(df: pd.DataFrame) -> pd.DataFrame:
+    """ta 패키지로 기술 지표 추가."""
+    from ta.momentum import RSIIndicator
+    from ta.trend import EMAIndicator, MACD, SMAIndicator
+    from ta.volatility import AverageTrueRange, BollingerBands
+
+    close = df["close"].astype(float)
+    high = df["high"].astype(float)
+    low = df["low"].astype(float)
+    vol = df["volume"].astype(float)
+
+    df["r1"] = close.pct_change()
+    df["rsi14"] = RSIIndicator(close=close, window=14, fillna=False).rsi()
+    macd = MACD(close=close, window_slow=26, window_fast=12, window_sign=9, fillna=False)
+    df["macd"] = macd.macd()
+    df["macd_signal"] = macd.macd_signal()
+    df["atr14"] = AverageTrueRange(high=high, low=low, close=close, window=14, fillna=False).average_true_range()
+    bb = BollingerBands(close=close, window=20, window_dev=2, fillna=False)
+    df["bb_pct"] = bb.bollinger_pband()
+    df["sma20"] = SMAIndicator(close=close, window=20, fillna=False).sma_indicator()
+    df["ema12"] = EMAIndicator(close=close, window=12, fillna=False).ema_indicator()
+    vol_mean = vol.rolling(20).mean()
+    vol_std = vol.rolling(20).std().replace(0, np.nan)
+    df["vol_z20"] = (vol - vol_mean) / vol_std
+    return df
+
+
+def _add_targets(df: pd.DataFrame, horizons: tuple[int, ...]) -> pd.DataFrame:
+    close = df["close"].astype(float)
+    for h in horizons:
+        df[f"y_close_h{h}"] = close.shift(-h)
+        df[f"y_ret_h{h}"] = df[f"y_close_h{h}"] / close - 1.0
+        df[f"y_dir_h{h}"] = np.where(
+            df[f"y_ret_h{h}"] > FLAT_BAND, 1,
+            np.where(df[f"y_ret_h{h}"] < -FLAT_BAND, -1, 0),
+        )
+    return df
+
+
+def build_features(
+    code: str,
+    *,
+    lookback_days: int = 365 * 2,
+    end_date: date | None = None,
+    horizons: tuple[int, ...] = HORIZONS_DEFAULT,
+    with_targets: bool = False,
+) -> FeatureFrame:
+    """code 1개 종목의 피처 DataFrame 생성.
+
+    inference: with_targets=False 로 호출 → 최신 row 의 피처만 LGBM/Chronos 에 투입.
+    training : with_targets=True 로 호출 → tail H 행은 타깃 NaN → dropna 로 제거.
+    """
+    end = end_date or date.today()
+    start = end - timedelta(days=lookback_days)
+
+    ohlcv = _load_ohlcv(code, start, end)
+    if ohlcv.empty:
+        return FeatureFrame(code=code, df=ohlcv, target_horizons=horizons)
+
+    df = ohlcv.copy().sort_values("date").reset_index(drop=True)
+
+    df = _add_ta(df)
+
+    trading = _load_trading(code, start, end)
+    if not trading.empty:
+        df = df.merge(trading, on="date", how="left")
+    else:
+        for col in ("foreign_net", "institution_net", "individual_net"):
+            df[col] = np.nan
+
+    macro = _load_macro(start, end)
+    if not macro.empty:
+        df = df.merge(macro, on="date", how="left")
+        for k in ("kospi", "kosdaq", "usdkrw", "us10y"):
+            if k in df.columns:
+                df[f"{k}_r1"] = df[k].pct_change()
+
+    sentiment = _load_sentiment(code, start, end)
+    if not sentiment.empty:
+        df = df.merge(sentiment, on="date", how="left")
+        # 3일 롤링 평균
+        for col in ("mean_score", "weighted_score", "pos_minus_neg", "n_articles"):
+            if col in df.columns:
+                df[f"{col}_3d"] = df[col].rolling(3, min_periods=1).mean()
+    else:
+        for col in ("n_articles", "mean_score", "pos_ratio", "neg_ratio",
+                    "weighted_score", "pos_minus_neg"):
+            df[col] = np.nan
+
+    if with_targets:
+        df = _add_targets(df, horizons)
+
+    return FeatureFrame(code=code, df=df, target_horizons=horizons)
+
+
+def feature_columns(df: pd.DataFrame) -> list[str]:
+    """LGBM 학습/추론용 피처 컬럼 목록. date / OHLCV / y_* 제외."""
+    drop = {"date", "open", "high", "low", "close", "volume"}
+    cols = [
+        c for c in df.columns
+        if c not in drop and not c.startswith("y_")
+    ]
+    return cols

backend/app/models/lgbm.py

@@ -0,0 +1,180 @@
+"""LightGBM 회귀 + 분류 모델. 종목 × horizon 별 별도 저장.
+
+- 회귀: target = y_ret_h{H}.  예측 후 base_close*(1+pred) 로 가격 환산.
+- 분류: target = y_dir_h{H} ∈ {-1, 0, +1}. 3-class softmax 로 prob_up/flat/down.
+
+저장 경로: backend/data/models/{code}_h{H}_reg.pkl, _cls.pkl  (joblib).
+"""
+from __future__ import annotations
+
+import logging
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+import joblib
+import numpy as np
+import pandas as pd
+
+from app.models.features import build_features, feature_columns
+
+logger = logging.getLogger(__name__)
+
+MODEL_DIR = Path(os.environ.get("LGBM_MODEL_DIR", "/app/data/models"))
+
+
+@dataclass
+class LgbmForecast:
+    horizon: int
+    base_close: float
+    predicted_close: float
+    predicted_return: float
+    prob_up: float
+    prob_flat: float
+    prob_down: float
+
+
+def _model_paths(code: str, horizon: int) -> tuple[Path, Path]:
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
+    return (
+        MODEL_DIR / f"{code}_h{horizon}_reg.pkl",
+        MODEL_DIR / f"{code}_h{horizon}_cls.pkl",
+    )
+
+
+def _prepare_xy(code: str, horizon: int, lookback_days: int) -> tuple[pd.DataFrame, pd.Series, pd.Series, list[str]]:
+    ff = build_features(
+        code,
+        lookback_days=lookback_days,
+        horizons=(horizon,),
+        with_targets=True,
+    )
+    df = ff.df
+    if df.empty:
+        return df, pd.Series(dtype=float), pd.Series(dtype=int), []
+    y_ret_col = f"y_ret_h{horizon}"
+    y_dir_col = f"y_dir_h{horizon}"
+    # 타깃 NaN (마지막 H 행) 제거.
+    df = df.dropna(subset=[y_ret_col, y_dir_col])
+    feats = feature_columns(df)
+    if not feats:
+        return df, pd.Series(dtype=float), pd.Series(dtype=int), []
+    X = df[feats]
+    # LightGBM 은 NaN 자체 처리 가능.
+    y_ret = df[y_ret_col].astype(float)
+    y_dir = df[y_dir_col].astype(int)
+    return X, y_ret, y_dir, feats
+
+
+def train_one(code: str, horizon: int, *, lookback_days: int = 365 * 3) -> dict:
+    """1종목 × 1 horizon 학습. 저장된 모델 파일 경로 + 샘플 수 반환."""
+    import lightgbm as lgb
+
+    X, y_ret, y_dir, feats = _prepare_xy(code, horizon, lookback_days)
+    if X.empty or len(X) < 100:
+        return {"code": code, "horizon": horizon, "status": "skipped_too_few_rows", "n_rows": int(len(X))}
+
+    reg_params = dict(
+        objective="regression",
+        learning_rate=0.05,
+        num_leaves=31,
+        min_data_in_leaf=20,
+        feature_fraction=0.85,
+        bagging_fraction=0.8,
+        bagging_freq=5,
+        verbose=-1,
+    )
+    cls_params = dict(
+        objective="multiclass",
+        num_class=3,
+        learning_rate=0.05,
+        num_leaves=31,
+        min_data_in_leaf=20,
+        feature_fraction=0.85,
+        bagging_fraction=0.8,
+        bagging_freq=5,
+        verbose=-1,
+    )
+
+    # 분류는 -1/0/1 → 0/1/2 인덱스로 매핑.
+    y_dir_idx = (y_dir + 1).astype(int)
+
+    n = len(X)
+    split = int(n * 0.85)
+    X_tr, X_val = X.iloc[:split], X.iloc[split:]
+    yr_tr, yr_val = y_ret.iloc[:split], y_ret.iloc[split:]
+    yc_tr, yc_val = y_dir_idx.iloc[:split], y_dir_idx.iloc[split:]
+
+    reg_train = lgb.Dataset(X_tr, label=yr_tr)
+    reg_valid = lgb.Dataset(X_val, label=yr_val, reference=reg_train)
+    reg_model = lgb.train(
+        reg_params,
+        reg_train,
+        num_boost_round=400,
+        valid_sets=[reg_valid],
+        callbacks=[lgb.early_stopping(stopping_rounds=30, verbose=False)],
+    )
+
+    cls_train = lgb.Dataset(X_tr, label=yc_tr)
+    cls_valid = lgb.Dataset(X_val, label=yc_val, reference=cls_train)
+    cls_model = lgb.train(
+        cls_params,
+        cls_train,
+        num_boost_round=400,
+        valid_sets=[cls_valid],
+        callbacks=[lgb.early_stopping(stopping_rounds=30, verbose=False)],
+    )
+
+    reg_path, cls_path = _model_paths(code, horizon)
+    joblib.dump({"model": reg_model, "features": feats}, reg_path)
+    joblib.dump({"model": cls_model, "features": feats}, cls_path)
+
+    return {
+        "code": code,
+        "horizon": horizon,
+        "status": "ok",
+        "n_rows": int(len(X)),
+        "reg_best_iter": int(reg_model.best_iteration or 0),
+        "cls_best_iter": int(cls_model.best_iteration or 0),
+        "reg_path": str(reg_path),
+        "cls_path": str(cls_path),
+    }
+
+
+def predict_one(code: str, horizon: int, *, lookback_days: int = 400) -> LgbmForecast | None:
+    """1종목 × 1 horizon 추론. 모델 없으면 None.
+
+    가장 최신 영업일 피처를 사용. base_close 는 그 행의 close.
+    """
+    reg_path, cls_path = _model_paths(code, horizon)
+    if not reg_path.exists() or not cls_path.exists():
+        return None
+    reg_blob = joblib.load(reg_path)
+    cls_blob = joblib.load(cls_path)
+    feats_reg = reg_blob["features"]
+    feats_cls = cls_blob["features"]
+    reg_model = reg_blob["model"]
+    cls_model = cls_blob["model"]
+
+    ff = build_features(code, lookback_days=lookback_days, horizons=(horizon,), with_targets=False)
+    df = ff.df
+    if df.empty:
+        return None
+    last = df.iloc[[-1]]
+    base_close = float(last["close"].iloc[0])
+    # 피처 정렬 (모델이 학습 당시 본 컬럼 순서대로).
+    X_reg = last.reindex(columns=feats_reg).fillna(value=np.nan)
+    X_cls = last.reindex(columns=feats_cls).fillna(value=np.nan)
+    pred_ret = float(reg_model.predict(X_reg)[0])
+    probs = cls_model.predict(X_cls)[0]
+    # 인덱스 0=-1(down), 1=0(flat), 2=+1(up)
+    prob_down, prob_flat, prob_up = float(probs[0]), float(probs[1]), float(probs[2])
+    return LgbmForecast(
+        horizon=horizon,
+        base_close=base_close,
+        predicted_close=base_close * (1.0 + pred_ret),
+        predicted_return=pred_ret,
+        prob_up=prob_up,
+        prob_flat=prob_flat,
+        prob_down=prob_down,
+    )

backend/app/models/weights.py

@@ -0,0 +1,75 @@
+"""ensemble_weights 테이블 IO. 기본 가중치 (chronos 0.6, lgbm 0.4)."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+
+@dataclass
+class EnsembleWeights:
+    code: str
+    horizon: int
+    w_chronos: float
+    w_lgbm: float
+
+
+DEFAULT_W_CHRONOS = 0.6
+DEFAULT_W_LGBM = 0.4
+
+
+def load_weights(code: str, horizon: int) -> EnsembleWeights:
+    eng = get_engine()
+    with eng.connect() as conn:
+        row = conn.execute(
+            text(
+                "SELECT w_chronos, w_lgbm FROM ensemble_weights "
+                "WHERE code = :code AND horizon = :h"
+            ),
+            {"code": code, "h": horizon},
+        ).first()
+    if not row:
+        return EnsembleWeights(code, horizon, DEFAULT_W_CHRONOS, DEFAULT_W_LGBM)
+    return EnsembleWeights(code, horizon, float(row[0]), float(row[1]))
+
+
+def upsert_weights(
+    code: str,
+    horizon: int,
+    w_chronos: float,
+    w_lgbm: float,
+    *,
+    hit_rate_chronos: float | None = None,
+    hit_rate_lgbm: float | None = None,
+    sample_count: int | None = None,
+) -> None:
+    eng = get_engine()
+    with eng.begin() as conn:
+        conn.execute(
+            text(
+                """
+                INSERT INTO ensemble_weights
+                  (code, horizon, w_chronos, w_lgbm, hit_rate_chronos, hit_rate_lgbm, sample_count, updated_at)
+                VALUES
+                  (:code, :h, :wc, :wl, :hc, :hl, :n, NOW())
+                ON CONFLICT (code, horizon) DO UPDATE SET
+                  w_chronos = EXCLUDED.w_chronos,
+                  w_lgbm    = EXCLUDED.w_lgbm,
+                  hit_rate_chronos = EXCLUDED.hit_rate_chronos,
+                  hit_rate_lgbm    = EXCLUDED.hit_rate_lgbm,
+                  sample_count     = EXCLUDED.sample_count,
+                  updated_at = NOW()
+                """
+            ),
+            {
+                "code": code,
+                "h": horizon,
+                "wc": float(w_chronos),
+                "wl": float(w_lgbm),
+                "hc": hit_rate_chronos,
+                "hl": hit_rate_lgbm,
+                "n": sample_count,
+            },
+        )

backend/app/nlp/finbert.py

@@ -0,0 +1,150 @@
+"""KR-FinBERT 감성 분석 어댑터.
+
+모델: snunlp/KR-FinBert-SC (3-class: negative / neutral / positive)
+
+- score      : prob(positive) - prob(negative)  ∈ [-1, +1]
+- label      : argmax 결과 ('positive' / 'neutral' / 'negative')
+- embedding  : 마지막 hidden state mean pool (768d) — `news.embedding` (VECTOR) 저장용
+
+디바이스: settings.model_device ('auto' → cuda 가용 시 cuda, 아니면 cpu).
+인증: settings.huggingface_token (gated 모델은 아니지만 HF rate limit 우회 + 일관성).
+캐시: HF_HOME=/root/.cache/huggingface (docker-compose 의 `hf_cache` 볼륨).
+
+lazy singleton — FastAPI 기동 시점에 모델을 로드하지 않고, 첫 score_texts() 호출
+또는 ping() 호출 시점에 로드.
+"""
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from dataclasses import dataclass
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+MODEL_NAME = "snunlp/KR-FinBert-SC"
+# KR-FinBert-SC 의 id2label : {0: 'negative', 1: 'neutral', 2: 'positive'}
+_LABELS = ("negative", "neutral", "positive")
+
+_lock = threading.Lock()
+_state: dict[str, object] = {
+    "loaded": False,
+    "tokenizer": None,
+    "model": None,
+    "device": None,
+}
+
+
+@dataclass
+class FinbertOutput:
+    label: str
+    score: float                # prob_positive - prob_negative ∈ [-1, +1]
+    prob_negative: float
+    prob_neutral: float
+    prob_positive: float
+    embedding: list[float]      # 768d mean-pooled last hidden state
+
+
+def _resolve_device() -> str:
+    """settings.model_device 값에 따라 'cuda'/'cpu' 결정."""
+    import torch  # lazy
+
+    pref = (settings.model_device or "auto").lower()
+    if pref == "cuda":
+        return "cuda" if torch.cuda.is_available() else "cpu"
+    if pref == "cpu":
+        return "cpu"
+    # 'auto'
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def _load() -> None:
+    global _state
+    with _lock:
+        if _state["loaded"]:
+            return
+        import torch
+        from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+        token = settings.huggingface_token or None
+        if token:
+            # transformers/datasets 모두 이 env 를 인식.
+            os.environ.setdefault("HUGGINGFACE_HUB_TOKEN", token)
+            os.environ.setdefault("HF_TOKEN", token)
+
+        device = _resolve_device()
+        logger.info("loading %s on %s", MODEL_NAME, device)
+        tok = AutoTokenizer.from_pretrained(MODEL_NAME, token=token)
+        mdl = AutoModelForSequenceClassification.from_pretrained(
+            MODEL_NAME,
+            token=token,
+            output_hidden_states=True,
+        )
+        mdl.eval()
+        mdl.to(device)
+        _state.update({"loaded": True, "tokenizer": tok, "model": mdl, "device": device})
+        logger.info("KR-FinBERT loaded (device=%s)", device)
+
+
+def score_texts(
+    texts: list[str],
+    *,
+    batch_size: int = 16,
+    max_length: int = 256,
+) -> list[FinbertOutput]:
+    """주어진 텍스트 리스트에 대해 감성 점수 + 라벨 + 768d embedding 반환.
+
+    빈 문자열은 placeholder('_')로 치환해서 라벨은 neutral 에 가깝게 나오게 함.
+    """
+    if not texts:
+        return []
+    _load()
+    import torch
+
+    tok = _state["tokenizer"]
+    mdl = _state["model"]
+    device = _state["device"]
+
+    results: list[FinbertOutput] = []
+    with torch.no_grad():
+        for i in range(0, len(texts), batch_size):
+            chunk = [(t or "").strip() or "_" for t in texts[i : i + batch_size]]
+            enc = tok(
+                chunk,
+                padding=True,
+                truncation=True,
+                max_length=max_length,
+                return_tensors="pt",
+            ).to(device)
+            out = mdl(**enc)
+            probs = torch.softmax(out.logits, dim=-1).cpu()
+            last_hidden = out.hidden_states[-1]                # (B, T, H)
+            mask = enc["attention_mask"].unsqueeze(-1).float()
+            pooled = (last_hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1.0)
+            pooled = pooled.cpu().tolist()
+
+            for row, vec in zip(probs.tolist(), pooled):
+                p_neg, p_neu, p_pos = row[0], row[1], row[2]
+                label_idx = int(max(range(3), key=lambda k: row[k]))
+                results.append(
+                    FinbertOutput(
+                        label=_LABELS[label_idx],
+                        score=float(p_pos - p_neg),
+                        prob_negative=float(p_neg),
+                        prob_neutral=float(p_neu),
+                        prob_positive=float(p_pos),
+                        embedding=[float(x) for x in vec],
+                    )
+                )
+    return results
+
+
+def ping() -> dict[str, object]:
+    """모델 로드 가능 여부 빠르게 확인. 한 번 로드되면 캐시됨."""
+    try:
+        _load()
+        return {"status": "ok", "model": MODEL_NAME, "device": _state["device"]}
+    except Exception as exc:  # noqa: BLE001
+        return {"status": "failed", "model": MODEL_NAME, "error": str(exc)}

backend/app/nlp/score_news.py

@@ -0,0 +1,96 @@
+"""DB news 테이블에서 sentiment_score IS NULL 인 행을 배치로 스코어링.
+
+refresh_one / daily_batch 에서 뉴스 upsert 직후 호출. 증분만 처리.
+"""
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+from app.nlp.finbert import score_texts
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ScoreResult:
+    fetched: int
+    scored: int
+    failed: int
+    error: str | None = None
+
+
+def _vector_literal(vec: list[float]) -> str:
+    """pgvector 텍스트 리터럴: '[v1,v2,...]' 형식. (:e)::vector 로 캐스팅."""
+    return "[" + ",".join(f"{x:.6f}" for x in vec) + "]"
+
+
+def score_pending_news(
+    *,
+    batch_size: int = 32,
+    limit: int | None = 500,
+    code: str | None = None,
+) -> ScoreResult:
+    """sentiment_score IS NULL 인 news 행에 대해 finbert score + label + embedding 채움.
+
+    Args:
+        batch_size: finbert inference 배치 크기.
+        limit:      한 번에 처리할 최대 행 수. None 이면 무제한.
+                    daily_batch 가 너무 무겁지 않도록 기본 500.
+        code:       특정 종목만 (None 이면 전체 무점수 행).
+    """
+    eng = get_engine()
+    where = "sentiment_score IS NULL"
+    params: dict[str, Any] = {}
+    if code:
+        where += " AND code = :code"
+        params["code"] = code
+
+    sql_select = (
+        "SELECT id, COALESCE(title, '') || ' ' || COALESCE(body, '') AS txt "
+        f"FROM news WHERE {where} ORDER BY id"
+    )
+    if limit is not None:
+        sql_select += f" LIMIT {int(limit)}"
+
+    with eng.connect() as conn:
+        rows = conn.execute(text(sql_select), params).all()
+    if not rows:
+        return ScoreResult(fetched=0, scored=0, failed=0)
+
+    ids = [r[0] for r in rows]
+    texts_in = [r[1] for r in rows]
+
+    try:
+        outputs = score_texts(texts_in, batch_size=batch_size)
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("score_texts failed")
+        return ScoreResult(fetched=len(rows), scored=0, failed=len(rows), error=str(exc))
+
+    update_sql = text(
+        "UPDATE news SET sentiment_score = :s, sentiment_label = :l, "
+        "embedding = (:e)::vector WHERE id = :id"
+    )
+    scored = 0
+    failed = 0
+    with eng.begin() as conn:
+        for nid, out in zip(ids, outputs):
+            try:
+                conn.execute(
+                    update_sql,
+                    {
+                        "id": nid,
+                        "s": out.score,
+                        "l": out.label,
+                        "e": _vector_literal(out.embedding),
+                    },
+                )
+                scored += 1
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("update news id=%s failed: %s", nid, exc)
+                failed += 1
+    return ScoreResult(fetched=len(rows), scored=scored, failed=failed)

backend/app/pipelines/daily_batch.py

@@ -11,6 +11,7 @@ import time
 from typing import Any
 
 from app.fetch import macro as macro_mod
+from app.nlp.score_news import score_pending_news
 from app.pipelines.refresh_one import refresh_code
 from app.seed.seed_tickers import SEED_TICKERS
 
@@ -32,11 +33,26 @@ def run_daily_batch() -> dict[str, Any]:
         for m in macros
     ]
 
+    # 시드 종목 refresh 내에서 종목당 200건만 스코어함. 잔여(여러 소스 합쳐
+    # 200건 초과 또는 코드 매핑 안된 google_rss 등)는 여기서 한 번에 mop-up.
+    try:
+        mop = score_pending_news(limit=2000)
+        sentiment_summary: dict[str, Any] = {
+            "status": "ok" if mop.error is None else "failed",
+            "fetched": mop.fetched,
+            "scored": mop.scored,
+            "failed": mop.failed,
+            "error": mop.error,
+        }
+    except Exception as exc:  # noqa: BLE001
+        sentiment_summary = {"status": "failed", "error": str(exc)}
+
     elapsed = time.time() - start_ts
     return {
         "duration_seconds": round(elapsed, 2),
         "tickers": reports,
         "macro": macro_summary,
+        "sentiment_mop": sentiment_summary,
     }
 
 

backend/app/pipelines/match_outcomes.py

@@ -0,0 +1,165 @@
+"""prediction_outcomes 매칭 배치.
+
+평일 16:30 KST 에 실행. 다음 거래일 장 종료 후 (KRX 정규장 마감 15:30) 의
+확정 종가가 16:00~16:30 사이 pykrx 로 들어온 뒤, target_date == 오늘인
+user_triggered=TRUE 예측을 그 종가와 매칭.
+
+cold-start / 휴장일 대비: 인자로 받은 target_date 의 ohlcv_daily 에 종가가
+없으면 자연스럽게 skip. 다음 거래일 매칭 잡이 다시 시도하면 그 날짜는
+여전히 매칭되지 않으므로 (매칭 sql 이 target_date 기준), 영원히 매칭 안되는
+잘못된 calendar date 예측은 cleanup CLI 로 별도 정리 가능 (Phase 7).
+"""
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from datetime import date, timedelta
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+logger = logging.getLogger(__name__)
+
+# direction_hit 판정 시 ±0.3% 이내는 flat. (features 의 FLAT_BAND 와 동일)
+FLAT_BAND = 0.003
+
+
+@dataclass
+class MatchSummary:
+    target_date: str
+    candidates: int
+    matched: int
+    skipped_no_actual: int
+    already_resolved: int
+
+
+def _direction_label(ret: float) -> str:
+    if ret > FLAT_BAND:
+        return "up"
+    if ret < -FLAT_BAND:
+        return "down"
+    return "flat"
+
+
+def match_for_date(d: date) -> MatchSummary:
+    """target_date == d 인 user_triggered=TRUE 예측을 매칭."""
+    eng = get_engine()
+    with eng.begin() as conn:
+        # 매칭 대상 예측 + 매칭 안 됐는지 확인.
+        candidate_rows = conn.execute(
+            text(
+                """
+                SELECT p.id, p.code, p.base_date, p.horizon, p.point_forecast,
+                       p.direction, p.model
+                  FROM predictions p
+             LEFT JOIN prediction_outcomes po ON po.prediction_id = p.id
+                 WHERE p.target_date = :d
+                   AND p.user_triggered = TRUE
+                   AND po.prediction_id IS NULL
+                """
+            ),
+            {"d": d},
+        ).all()
+        candidates = len(candidate_rows)
+        if not candidates:
+            return MatchSummary(str(d), 0, 0, 0, 0)
+
+        # 종목별로 actual close 조회 (한번에 batch).
+        codes = list({r[1] for r in candidate_rows})
+        actual_map: dict[tuple[str, date], float] = {}
+        for code in codes:
+            row = conn.execute(
+                text(
+                    "SELECT close FROM ohlcv_daily WHERE code = :c AND date = :d"
+                ),
+                {"c": code, "d": d},
+            ).first()
+            if row and row[0] is not None:
+                actual_map[(code, d)] = float(row[0])
+
+        # base_close (각 예측의 base_date 종가) 도 필요 — direction 판정용.
+        base_close_map: dict[tuple[str, date], float] = {}
+        for pid, code, base_date, *_ in candidate_rows:
+            key = (code, base_date)
+            if key in base_close_map:
+                continue
+            row = conn.execute(
+                text("SELECT close FROM ohlcv_daily WHERE code = :c AND date = :d"),
+                {"c": code, "d": base_date},
+            ).first()
+            if row and row[0] is not None:
+                base_close_map[key] = float(row[0])
+
+        matched = 0
+        skipped = 0
+        already = 0
+        for pid, code, base_date, horizon, point_forecast, pred_dir, model in candidate_rows:
+            actual = actual_map.get((code, d))
+            base_close = base_close_map.get((code, base_date))
+            if actual is None or base_close is None:
+                skipped += 1
+                continue
+            actual_ret = actual / base_close - 1.0
+            actual_dir = _direction_label(actual_ret)
+            dir_hit = (pred_dir == actual_dir)
+            abs_err = abs(float(point_forecast) - actual) if point_forecast is not None else None
+
+            try:
+                conn.execute(
+                    text(
+                        """
+                        INSERT INTO prediction_outcomes
+                          (prediction_id, code, target_date, horizon, model,
+                           predicted_close, actual_close, actual_return, direction_hit, abs_error)
+                        VALUES
+                          (:pid, :code, :d, :h, :m, :pc, :ac, :ar, :dh, :ae)
+                        ON CONFLICT (prediction_id) DO NOTHING
+                        """
+                    ),
+                    {
+                        "pid": pid,
+                        "code": code,
+                        "d": d,
+                        "h": horizon,
+                        "m": model,
+                        "pc": float(point_forecast) if point_forecast is not None else None,
+                        "ac": actual,
+                        "ar": float(actual_ret),
+                        "dh": bool(dir_hit),
+                        "ae": float(abs_err) if abs_err is not None else None,
+                    },
+                )
+                matched += 1
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("match insert failed pid=%s: %s", pid, exc)
+                already += 1
+
+        return MatchSummary(
+            target_date=str(d),
+            candidates=candidates,
+            matched=matched,
+            skipped_no_actual=skipped,
+            already_resolved=already,
+        )
+
+
+def match_today() -> dict[str, Any]:
+    """평일 16:30 KST 호출용. target_date == today (KST) 인 행 매칭."""
+    from datetime import datetime, timezone, timedelta as td
+
+    kst = timezone(td(hours=9))
+    today = datetime.now(kst).date()
+    summary = match_for_date(today)
+    return {
+        "today": str(today),
+        "summary": summary.__dict__,
+    }
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    out = match_today()
+    print(json.dumps(out, ensure_ascii=False, indent=2, default=str))

backend/app/pipelines/predict_one.py

@@ -0,0 +1,138 @@
+"""On-demand 예측 + DB 적재.
+
+POST /api/predict/{code} 에서 호출. 사용자가 "예상차트 보기" 누른 시점.
+- ensemble.predict() 로 horizons (1,3,5) 결과 계산
+- base_date = 마지막 ohlcv_daily.date, target_date = base_date + horizon 영업일
+  (대충 calendar 일로 +h * 1.4 — KRX 영업일 추정. Phase 4 단순화: base_date + h 영업일은
+   ohlcv 상의 다음 h 거래일이 아닌, "거래일 카운트" 대신 단순 calendar+h 로 저장하고
+   매칭 잡에서 ohlcv_daily 에 그 날짜 행이 있는지로 자연 보정.)
+
+대안 정확도 위해: 매칭 잡은 "예측의 target_date 이 오늘"인 행을 그날 종가와 비교.
+calendar date 가 비거래일이면 매칭이 안 되니, 매칭 잡은 매일 실행되어 모일 때 처리.
+"""
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import asdict
+from datetime import date, datetime, timedelta, timezone
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+from app.models.ensemble import EnsemblePrediction, predict as ensemble_predict
+
+logger = logging.getLogger(__name__)
+
+KST = timezone(timedelta(hours=9))
+
+
+def _next_trading_target(base_date: date, horizon: int) -> date:
+    """base_date + horizon 거래일 (주말만 스킵, 공휴일은 무시 — 매칭잡이 자연 보정)."""
+    d = base_date
+    added = 0
+    while added < horizon:
+        d = d + timedelta(days=1)
+        if d.weekday() < 5:  # 0..4 = Mon..Fri
+            added += 1
+    return d
+
+
+def _last_trading_date(code: str) -> date | None:
+    eng = get_engine()
+    with eng.connect() as conn:
+        row = conn.execute(
+            text("SELECT MAX(date) FROM ohlcv_daily WHERE code = :c"),
+            {"c": code},
+        ).first()
+    return row[0] if row and row[0] else None
+
+
+def predict_and_store(
+    code: str,
+    *,
+    horizons: tuple[int, ...] = (1, 3, 5),
+    user_triggered: bool = True,
+) -> dict[str, Any]:
+    """앙상블 예측 실행 + predictions 테이블 적재. 결과 JSON-serializable dict 반환."""
+    base_date = _last_trading_date(code)
+    if base_date is None:
+        raise RuntimeError(f"no ohlcv_daily for {code}; refresh first")
+
+    pred: EnsemblePrediction = ensemble_predict(code, horizons=horizons)
+    now = datetime.now(KST)
+
+    eng = get_engine()
+    saved_ids: list[int] = []
+    with eng.begin() as conn:
+        for step in pred.steps:
+            target_date = _next_trading_target(base_date, step.horizon)
+            features_snap = {
+                "base_close": pred.base_close,
+                "sources_used": pred.sources_used,
+                "direction": step.direction,
+            }
+            row = conn.execute(
+                text(
+                    """
+                    INSERT INTO predictions
+                      (code, predicted_at, base_date, target_date, horizon, model,
+                       direction, prob_up, prob_flat, prob_down, expected_return,
+                       point_forecast, ci_low, ci_high, features_snapshot, user_triggered)
+                    VALUES
+                      (:code, :predicted_at, :base_date, :target_date, :horizon, 'ensemble',
+                       :direction, :p_up, :p_fl, :p_dn, :exp_ret,
+                       :point, :lo, :hi, CAST(:feats AS JSONB), :ut)
+                    ON CONFLICT (code, base_date, target_date, horizon, model)
+                    DO UPDATE SET
+                      predicted_at = EXCLUDED.predicted_at,
+                      direction    = EXCLUDED.direction,
+                      prob_up      = EXCLUDED.prob_up,
+                      prob_flat    = EXCLUDED.prob_flat,
+                      prob_down    = EXCLUDED.prob_down,
+                      expected_return = EXCLUDED.expected_return,
+                      point_forecast  = EXCLUDED.point_forecast,
+                      ci_low          = EXCLUDED.ci_low,
+                      ci_high         = EXCLUDED.ci_high,
+                      features_snapshot = EXCLUDED.features_snapshot,
+                      user_triggered = predictions.user_triggered OR EXCLUDED.user_triggered
+                    RETURNING id
+                    """
+                ),
+                {
+                    "code": code,
+                    "predicted_at": now,
+                    "base_date": base_date,
+                    "target_date": target_date,
+                    "horizon": step.horizon,
+                    "direction": step.direction,
+                    "p_up": step.prob_up,
+                    "p_fl": step.prob_flat,
+                    "p_dn": step.prob_down,
+                    "exp_ret": step.expected_return,
+                    "point": step.point_close,
+                    "lo": step.ci_low,
+                    "hi": step.ci_high,
+                    "feats": json.dumps(features_snap),
+                    "ut": user_triggered,
+                },
+            ).first()
+            if row:
+                saved_ids.append(int(row[0]))
+
+    return {
+        "code": code,
+        "base_date": str(base_date),
+        "base_close": pred.base_close,
+        "sources_used": pred.sources_used,
+        "steps": [
+            {
+                **asdict(s),
+                "target_date": str(_next_trading_target(base_date, s.horizon)),
+            }
+            for s in pred.steps
+        ],
+        "saved_prediction_ids": saved_ids,
+        "user_triggered": user_triggered,
+    }

backend/app/pipelines/refresh_one.py

@@ -36,6 +36,7 @@ class RefreshReport:
     dart: SourceStatus
     naver_news: SourceStatus
     google_rss: SourceStatus
+    finbert: SourceStatus
 
     def to_dict(self) -> dict[str, Any]:
         out: dict[str, Any] = {"code": self.code}
@@ -46,6 +47,7 @@ class RefreshReport:
             "dart",
             "naver_news",
             "google_rss",
+            "finbert",
         ):
             v: SourceStatus = getattr(self, f)
             out[f] = asdict(v)
@@ -132,6 +134,25 @@ def _google_rss(code: str, name: str) -> SourceStatus:
         return SourceStatus(status="failed", error=str(exc))
 
 
+def _finbert(code: str) -> SourceStatus:
+    """방금 upsert 된 뉴스 중 sentiment_score 가 비어있는 행을 KR-FinBERT 로 스코어."""
+    try:
+        from app.nlp.score_news import score_pending_news
+
+        # 한 종목에 대해 신규 뉴스가 매우 많아도 200건으로 컷.
+        # daily_batch 끝에서 잔여분을 별도로 mop-up 한다.
+        res = score_pending_news(code=code, limit=200)
+        return SourceStatus(
+            status="ok" if res.error is None else "failed",
+            inserted=res.scored,
+            skipped=res.failed,
+            extra={"fetched": res.fetched},
+            error=res.error,
+        )
+    except Exception as exc:  # noqa: BLE001
+        return SourceStatus(status="failed", error=str(exc))
+
+
 def refresh_code(code: str, name: str, *, lookback_days: int = 7) -> RefreshReport:
     """단기 갱신 (daily_batch 용). 최근 lookback_days 만 가져온다."""
     end = date.today()
@@ -144,4 +165,5 @@ def refresh_code(code: str, name: str, *, lookback_days: int = 7) -> RefreshRepo
         dart=_dart(code, start, end),
         naver_news=_naver_news(code),
         google_rss=_google_rss(code, name),
+        finbert=_finbert(code),
     )

backend/app/pipelines/retrain_weekly.py

@@ -0,0 +1,122 @@
+"""주간 재학습 + 앙상블 가중치 보정.
+
+일요일 02:00 KST 실행:
+  1. 시드 10종목 × horizon (1,3,5) 별로 LGBM 학습 (train_one).
+  2. 최근 30일 prediction_outcomes 의 model 별 hit_rate 산출, model_performance 적재.
+  3. 같은 30일 윈도우에서 chronos vs lgbm hit_rate 로 ensemble_weights 갱신.
+     (방법: w_chronos = clamp(0.1, hr_c / (hr_c + hr_l), 0.9), w_lgbm = 1 - w_chronos.
+      hit_rate 데이터가 부족하면 default 0.6/0.4 유지.)
+
+지금은 'ensemble' 모델 단일 종류로 predictions 가 쌓이므로, 가중치 보정은
+chronos 단독 시뮬레이션 / lgbm 단독 시뮬레이션 hit_rate 비교가 진정한 방식인데,
+Phase 4 단순화: 'ensemble' 의 종합 hit_rate 만 model_performance 에 기록하고
+가중치는 default 유지. 진짜 비교는 Phase 7 (chronos 단독 + lgbm 단독 예측을
+shadow 로 같이 적재하는 구조) 로 확장.
+"""
+from __future__ import annotations
+
+import json
+import logging
+from datetime import date, timedelta
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+from app.models.lgbm import train_one
+from app.seed.seed_tickers import SEED_TICKERS
+
+logger = logging.getLogger(__name__)
+
+HORIZONS = (1, 3, 5)
+WINDOW_DAYS = 30
+
+
+def retrain_all() -> list[dict[str, Any]]:
+    """시드 10종목 × horizons 학습."""
+    out: list[dict[str, Any]] = []
+    for t in SEED_TICKERS:
+        for h in HORIZONS:
+            try:
+                res = train_one(t.code, h)
+            except Exception as exc:  # noqa: BLE001
+                logger.exception("train_one failed for %s h=%d", t.code, h)
+                res = {"code": t.code, "horizon": h, "status": "failed", "error": str(exc)}
+            out.append(res)
+    return out
+
+
+def record_performance(as_of: date) -> list[dict[str, Any]]:
+    """최근 WINDOW_DAYS 의 prediction_outcomes 로 (code, model, horizon) 별
+    hit_rate / mae 산출, model_performance 에 upsert."""
+    eng = get_engine()
+    start = as_of - timedelta(days=WINDOW_DAYS)
+    summary: list[dict[str, Any]] = []
+    with eng.begin() as conn:
+        rows = conn.execute(
+            text(
+                """
+                SELECT code, model, horizon,
+                       AVG(CASE WHEN direction_hit THEN 1.0 ELSE 0.0 END) AS hit_rate,
+                       AVG(abs_error)                                     AS mae,
+                       COUNT(*)                                           AS n
+                  FROM prediction_outcomes
+                 WHERE resolved_at >= :start
+                 GROUP BY code, model, horizon
+                """
+            ),
+            {"start": start},
+        ).all()
+        for code, model, horizon, hr, mae, n in rows:
+            conn.execute(
+                text(
+                    """
+                    INSERT INTO model_performance
+                      (code, model, window_days, as_of, hit_rate, mae, sample_count)
+                    VALUES (:c, :m, :w, :as_of, :hr, :mae, :n)
+                    ON CONFLICT (code, model, window_days, as_of) DO UPDATE SET
+                      hit_rate = EXCLUDED.hit_rate,
+                      mae      = EXCLUDED.mae,
+                      sample_count = EXCLUDED.sample_count
+                    """
+                ),
+                {
+                    "c": code,
+                    "m": model,
+                    "w": WINDOW_DAYS,
+                    "as_of": as_of,
+                    "hr": float(hr) if hr is not None else None,
+                    "mae": float(mae) if mae is not None else None,
+                    "n": int(n),
+                },
+            )
+            summary.append(
+                {
+                    "code": code,
+                    "model": model,
+                    "horizon": horizon,
+                    "hit_rate": float(hr) if hr is not None else None,
+                    "mae": float(mae) if mae is not None else None,
+                    "n": int(n),
+                }
+            )
+    return summary
+
+
+def run_weekly() -> dict[str, Any]:
+    """일요일 02:00 KST 호출 entry-point."""
+    from datetime import datetime, timezone
+
+    kst = timezone(timedelta(hours=9))
+    as_of = datetime.now(kst).date()
+    return {
+        "as_of": str(as_of),
+        "trained": retrain_all(),
+        "performance": record_performance(as_of),
+    }
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    out = run_weekly()
+    print(json.dumps(out, ensure_ascii=False, indent=2, default=str))

backend/app/pipelines/scheduler.py

@@ -22,6 +22,8 @@ from apscheduler.triggers.cron import CronTrigger
 from pytz import timezone
 
 from app.pipelines.daily_batch import run_daily_batch
+from app.pipelines.match_outcomes import match_today
+from app.pipelines.retrain_weekly import run_weekly
 
 logger = logging.getLogger(__name__)
 KST = timezone("Asia/Seoul")
@@ -34,15 +36,34 @@ def start_scheduler() -> BackgroundScheduler:
     if _scheduler:
         return _scheduler
     _scheduler = BackgroundScheduler(timezone=KST)
+    # 16:00 평일: 시드 10종목 EOD/뉴스/공시/거시 갱신
     _scheduler.add_job(
         run_daily_batch,
-        CronTrigger(hour=16, minute=0, timezone=KST),
+        CronTrigger(day_of_week="mon-fri", hour=16, minute=0, timezone=KST),
         id="daily_batch_16",
         replace_existing=True,
         max_instances=1,
     )
+    # 16:30 평일: prediction_outcomes 매칭 배치
+    _scheduler.add_job(
+        match_today,
+        CronTrigger(day_of_week="mon-fri", hour=16, minute=30, timezone=KST),
+        id="match_outcomes_1630",
+        replace_existing=True,
+        max_instances=1,
+    )
+    # 일요일 02:00: LGBM 재학습 + 성능 기록
+    _scheduler.add_job(
+        run_weekly,
+        CronTrigger(day_of_week="sun", hour=2, minute=0, timezone=KST),
+        id="retrain_weekly_sun_0200",
+        replace_existing=True,
+        max_instances=1,
+    )
     _scheduler.start()
-    logger.info("scheduler started (daily_batch @ 16:00 KST)")
+    logger.info(
+        "scheduler started: daily_batch(16:00 mon-fri), match_outcomes(16:30 mon-fri), retrain_weekly(sun 02:00) KST"
+    )
     return _scheduler
 
 

backend/pyproject.toml

@@ -31,9 +31,12 @@ dependencies = [
     "transformers==4.41.2",
     "tokenizers==0.19.1",
     "sentencepiece==0.2.0",
+    "accelerate==0.30.1",
+    "chronos-forecasting==1.4.1",
     "scikit-learn==1.5.0",
     "lightgbm==4.3.0",
     "ta==0.11.0",
+    "joblib==1.4.2",
 
     # scheduler
     "apscheduler==3.10.4",
@@ -63,3 +66,19 @@ include = ["app*"]
 [tool.ruff]
 line-length = 100
 target-version = "py311"
+
+[tool.mypy]
+python_version = "3.11"
+strict_optional = true
+warn_unused_ignores = true
+warn_redundant_casts = true
+warn_unreachable = true
+ignore_missing_imports = true   # 3rd-party stub 부족 무시 (pykrx, chronos, ta, feedparser ...)
+no_implicit_optional = true
+show_error_codes = true
+exclude = ["build", "dist", ".venv"]
+
+[[tool.mypy.overrides]]
+module = ["app.*"]
+disallow_untyped_defs = false  # 점진적 도입. 핵심 모듈만 strict 로 올릴 예정.
+check_untyped_defs = true

web/.eslintrc.json

@@ -0,0 +1,6 @@
+{
+  "extends": "next/core-web-vitals",
+  "rules": {
+    "react-hooks/exhaustive-deps": "warn"
+  }
+}

web/app/[code]/page.tsx

@@ -0,0 +1,99 @@
+"use client";
+
+import Link from "next/link";
+import { useEffect, useState } from "react";
+import { MetricsPanel } from "../../components/MetricsPanel";
+import { NewsList } from "../../components/NewsList";
+import { PredictionPanel } from "../../components/PredictionPanel";
+import { StockChart } from "../../components/StockChart";
+import {
+  api,
+  type ChartPayload,
+  type LatestPredictionResponse,
+} from "../../lib/api";
+
+export default function CodePage({ params }: { params: { code: string } }) {
+  const { code } = params;
+  const [chart, setChart] = useState<ChartPayload | null>(null);
+  const [prediction, setPrediction] = useState<LatestPredictionResponse | null>(null);
+  const [err, setErr] = useState<string | null>(null);
+  const [days, setDays] = useState(180);
+
+  useEffect(() => {
+    let alive = true;
+    setErr(null);
+    setChart(null);
+    api
+      .getChart(code, days)
+      .then((c) => {
+        if (alive) setChart(c);
+      })
+      .catch((e) => {
+        if (alive) setErr(e instanceof Error ? e.message : String(e));
+      });
+    return () => {
+      alive = false;
+    };
+  }, [code, days]);
+
+  useEffect(() => {
+    let alive = true;
+    api
+      .latestPrediction(code)
+      .then((r) => {
+        if (alive && r.found) setPrediction(r);
+      })
+      .catch(() => {
+        // 예측 이력 없는 경우는 무시.
+      });
+    return () => {
+      alive = false;
+    };
+  }, [code]);
+
+  return (
+    <main className="mx-auto max-w-5xl px-6 py-10">
+      <div className="mb-4 flex items-center justify-between">
+        <Link href="/" className="text-xs text-zinc-500 hover:text-zinc-300">
+          ← 검색으로
+        </Link>
+        <select
+          value={days}
+          onChange={(e) => setDays(Number(e.target.value))}
+          className="rounded-md border border-zinc-700 bg-zinc-900 px-2 py-1 text-xs"
+        >
+          <option value={60}>최근 3개월</option>
+          <option value={180}>최근 6개월</option>
+          <option value={365}>최근 1년</option>
+          <option value={1095}>최근 3년</option>
+        </select>
+      </div>
+
+      {chart && (
+        <div className="mb-4">
+          <h1 className="text-2xl font-semibold text-zinc-100">
+            {chart.name}{" "}
+            <span className="text-sm font-normal text-zinc-500">
+              {chart.code} · {chart.market}
+            </span>
+          </h1>
+        </div>
+      )}
+
+      {err && <div className="mb-4 text-sm text-red-400">차트 로딩 실패: {err}</div>}
+
+      {chart && (
+        <>
+          <StockChart chart={chart} prediction={prediction} />
+          <div className="mt-6">
+            <PredictionPanel code={code} initial={prediction} onResult={setPrediction} />
+          </div>
+          <div className="mt-6 grid gap-6 md:grid-cols-2">
+            <MetricsPanel code={code} />
+            <NewsList code={code} />
+          </div>
+        </>
+      )}
+    </main>
+  );
+}

web/app/page.tsx

@@ -1,13 +1,32 @@
+import { SearchBox } from "../components/SearchBox";
+
 export default function HomePage() {
   return (
     <main className="mx-auto max-w-3xl px-6 py-16">
       <h1 className="text-3xl font-bold tracking-tight">Stock Chart Site</h1>
-      <p className="mt-3 text-sm text-zinc-400">
-        Phase 0 scaffold. 종목 검색 UI는 Phase 6에서 추가됩니다.
+      <p className="mt-2 text-sm text-zinc-400">
+        종목을 검색해 현재 차트를 보고, <b>예상차트 보기</b> 버튼으로 Chronos + LightGBM
+        앙상블의 단기(1·3·5거래일) 예측을 차트에 이어 붙입니다.
       </p>
-      <div className="mt-8 rounded-md border border-zinc-800 bg-zinc-900/50 p-4 text-sm">
-        <div className="font-medium">Backend health</div>
-        <code className="mt-2 block text-zinc-400">GET {process.env.NEXT_PUBLIC_API_BASE}/health</code>
+
+      <div className="mt-8">
+        <SearchBox autoFocus />
+      </div>
+
+      <div className="mt-12 grid gap-3 text-xs text-zinc-500 sm:grid-cols-2">
+        <div className="rounded-md border border-zinc-800 bg-zinc-900/30 p-4">
+          <div className="font-medium text-zinc-300">학습 대상 10종목</div>
+          <div className="mt-1">
+            삼성전자, SK하이닉스, 에코프로비엠, 한미반도체, 두산에너빌리티, 한화에어로스페이스,
+            HD현대중공업, NAVER, KT&amp;G, 한국가스공사
+          </div>
+        </div>
+        <div className="rounded-md border border-zinc-800 bg-zinc-900/30 p-4">
+          <div className="font-medium text-zinc-300">매칭/재학습</div>
+          <div className="mt-1">
+            평일 16:30 KST 에 예측과 실제 종가 매칭, 일요일 02:00 KST 에 LGBM 재학습.
+          </div>
+        </div>
       </div>
     </main>
   );

web/components/MetricsPanel.tsx

@@ -0,0 +1,64 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { api, type MetricsResponse } from "../lib/api";
+
+export function MetricsPanel({ code }: { code: string }) {
+  const [m, setM] = useState<MetricsResponse | null>(null);
+  const [err, setErr] = useState<string | null>(null);
+
+  useEffect(() => {
+    let alive = true;
+    api
+      .metrics(code, 30)
+      .then((r) => {
+        if (alive) setM(r);
+      })
+      .catch((e) => {
+        if (alive) setErr(e instanceof Error ? e.message : String(e));
+      });
+    return () => {
+      alive = false;
+    };
+  }, [code]);
+
+  if (err) return <div className="text-xs text-red-400">메트릭 로딩 실패: {err}</div>;
+  if (!m) return <div className="text-xs text-zinc-500">메트릭 로딩 중…</div>;
+
+  const rows = m.by_model_horizon ?? [];
+  if (!rows.length) {
+    return (
+      <div className="text-xs text-zinc-500">
+        최근 30일 매칭된 예측 결과가 아직 없습니다. (매칭 배치는 평일 16:30 KST 에 실행)
+      </div>
+    );
+  }
+
+  return (
+    <div className="rounded-md border border-zinc-800 bg-zinc-900/40 p-4">
+      <div className="mb-2 text-sm font-medium text-zinc-200">최근 30일 모델 성능</div>
+      <table className="w-full text-left text-sm">
+        <thead className="text-xs text-zinc-500">
+          <tr>
+            <th className="py-1">모델</th>
+            <th>+거래일</th>
+            <th>표본 수</th>
+            <th>방향 적중률</th>
+            <th>평균 절대오차 (MAE)</th>
+          </tr>
+        </thead>
+        <tbody className="divide-y divide-zinc-800">
+          {rows.map((r, i) => (
+            <tr key={i}>
+              <td className="py-1">{r.model}</td>
+              <td>+{r.horizon}</td>
+              <td>{r.n}</td>
+              <td>{r.hit_rate != null ? `${(r.hit_rate * 100).toFixed(1)}%` : "-"}</td>
+              <td>{r.mae != null ? r.mae.toLocaleString(undefined, { maximumFractionDigits: 1 }) : "-"}</td>
+            </tr>
+          ))}
+        </tbody>
+      </table>
+    </div>
+  );
+}

web/components/NewsList.tsx

@@ -0,0 +1,77 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { api, type NewsResponse } from "../lib/api";
+
+export function NewsList({ code }: { code: string }) {
+  const [data, setData] = useState<NewsResponse | null>(null);
+  const [err, setErr] = useState<string | null>(null);
+
+  useEffect(() => {
+    let alive = true;
+    api
+      .news(code, 20)
+      .then((r) => {
+        if (alive) setData(r);
+      })
+      .catch((e) => {
+        if (alive) setErr(e instanceof Error ? e.message : String(e));
+      });
+    return () => {
+      alive = false;
+    };
+  }, [code]);
+
+  if (err) return <div className="text-xs text-red-400">뉴스 로딩 실패: {err}</div>;
+  if (!data) return <div className="text-xs text-zinc-500">뉴스 로딩 중…</div>;
+  if (!data.items.length)
+    return <div className="text-xs text-zinc-500">최근 뉴스 없음</div>;
+
+  return (
+    <div className="rounded-md border border-zinc-800 bg-zinc-900/40 p-4">
+      <div className="mb-2 text-sm font-medium text-zinc-200">최근 뉴스/공시</div>
+      <ul className="divide-y divide-zinc-800">
+        {data.items.map((n, i) => (
+          <li key={i} className="py-2">
+            <a
+              href={n.url}
+              target="_blank"
+              rel="noopener noreferrer"
+              className="block hover:bg-zinc-800/40"
+            >
+              <div className="text-sm text-zinc-100 line-clamp-2">{n.title}</div>
+              <div className="mt-0.5 flex items-center gap-2 text-xs text-zinc-500">
+                <span>{n.source}</span>
+                {n.published_at && <span>· {formatDate(n.published_at)}</span>}
+                {n.sentiment_label && (
+                  <span className={sentimentColor(n.sentiment_label)}>
+                    · {n.sentiment_label} {n.sentiment_score != null ? `(${n.sentiment_score.toFixed(2)})` : ""}
+                  </span>
+                )}
+              </div>
+            </a>
+          </li>
+        ))}
+      </ul>
+    </div>
+  );
+}
+
+function sentimentColor(l: string): string {
+  if (l === "positive") return "text-emerald-400";
+  if (l === "negative") return "text-red-400";
+  return "text-zinc-400";
+}
+
+function formatDate(iso: string): string {
+  try {
+    const d = new Date(iso);
+    return `${d.getFullYear()}-${pad(d.getMonth() + 1)}-${pad(d.getDate())} ${pad(d.getHours())}:${pad(d.getMinutes())}`;
+  } catch {
+    return iso;
+  }
+}
+
+function pad(n: number): string {
+  return n < 10 ? `0${n}` : `${n}`;
+}

web/components/PredictionPanel.tsx

@@ -0,0 +1,157 @@
+"use client";
+
+import { useState } from "react";
+import {
+  api,
+  type LatestPredictionResponse,
+  type LatestPredictionStep,
+  type PredictResponse,
+} from "../lib/api";
+
+type Props = {
+  code: string;
+  initial?: LatestPredictionResponse | null;
+  onResult: (pred: LatestPredictionResponse) => void;
+};
+
+function normalizeFromPredictResponse(
+  code: string,
+  resp: PredictResponse,
+): LatestPredictionResponse {
+  const steps: LatestPredictionStep[] = resp.steps.map((s) => ({
+    predicted_at: null,
+    target_date: s.target_date ?? "",
+    horizon: s.horizon,
+    direction: s.direction,
+    prob_up: s.prob_up,
+    prob_flat: s.prob_flat,
+    prob_down: s.prob_down,
+    expected_return: s.expected_return,
+    point_close: s.point_close,
+    ci_low: s.ci_low,
+    ci_high: s.ci_high,
+    user_triggered: resp.user_triggered,
+    features_snapshot: null,
+  }));
+  return {
+    code,
+    found: true,
+    base_date: resp.base_date,
+    base_close: resp.base_close,
+    steps,
+  };
+}
+
+export function PredictionPanel({ code, initial, onResult }: Props) {
+  const [pred, setPred] = useState<LatestPredictionResponse | null>(initial ?? null);
+  const [loading, setLoading] = useState(false);
+  const [err, setErr] = useState<string | null>(null);
+
+  async function runPredict() {
+    setLoading(true);
+    setErr(null);
+    try {
+      const r = await api.predict(code);
+      const normalized = normalizeFromPredictResponse(code, r);
+      setPred(normalized);
+      onResult(normalized);
+    } catch (e) {
+      setErr(e instanceof Error ? e.message : String(e));
+    } finally {
+      setLoading(false);
+    }
+  }
+
+  const steps = pred?.steps ?? [];
+
+  return (
+    <div className="rounded-md border border-zinc-800 bg-zinc-900/40 p-4">
+      <div className="mb-3 flex items-center justify-between">
+        <div>
+          <div className="text-sm font-medium text-zinc-200">예측 (Chronos + LightGBM 앙상블)</div>
+          <div className="text-xs text-zinc-500">
+            클릭한 종목은 자동 저장 후 다음 거래일 장 종료 시 실제 가격과 비교됩니다.
+          </div>
+        </div>
+        <button
+          onClick={runPredict}
+          disabled={loading}
+          className="rounded-md bg-emerald-700 px-4 py-2 text-sm font-medium text-white hover:bg-emerald-600 disabled:opacity-50"
+        >
+          {loading ? "예측 중…" : pred?.found ? "다시 예측" : "예상차트 보기"}
+        </button>
+      </div>
+
+      {err && <div className="mb-3 text-xs text-red-400">에러: {err}</div>}
+
+      {pred?.found ? (
+        <div>
+          <div className="mb-2 text-xs text-zinc-500">
+            기준일 {pred.base_date} · 기준종가{" "}
+            {pred.base_close != null ? pred.base_close.toLocaleString() : "-"}
+          </div>
+          <table className="w-full text-left text-sm">
+            <thead className="text-xs text-zinc-500">
+              <tr>
+                <th className="py-1">+거래일</th>
+                <th>매칭일</th>
+                <th>방향</th>
+                <th>P(up/flat/down)</th>
+                <th>기대수익</th>
+                <th>예측 종가</th>
+                <th>q10~q90</th>
+              </tr>
+            </thead>
+            <tbody className="divide-y divide-zinc-800">
+              {steps.map((s) => (
+                <tr key={s.horizon}>
+                  <td className="py-2">+{s.horizon}</td>
+                  <td className="text-xs text-zinc-400">{s.target_date}</td>
+                  <td>
+                    <span
+                      className={
+                        s.direction === "up"
+                          ? "text-emerald-400"
+                          : s.direction === "down"
+                            ? "text-red-400"
+                            : "text-zinc-300"
+                      }
+                    >
+                      {s.direction}
+                    </span>
+                  </td>
+                  <td className="text-xs text-zinc-300">
+                    {fmtPct(s.prob_up)} / {fmtPct(s.prob_flat)} / {fmtPct(s.prob_down)}
+                  </td>
+                  <td>{fmtSignedPct(s.expected_return)}</td>
+                  <td>{s.point_close != null ? s.point_close.toLocaleString() : "-"}</td>
+                  <td className="text-xs text-zinc-400">
+                    {s.ci_low != null ? s.ci_low.toLocaleString() : "-"} ~{" "}
+                    {s.ci_high != null ? s.ci_high.toLocaleString() : "-"}
+                  </td>
+                </tr>
+              ))}
+            </tbody>
+          </table>
+        </div>
+      ) : (
+        <div className="text-xs text-zinc-500">
+          아직 예측이 없습니다. <b>예상차트 보기</b> 버튼을 누르면 1·3·5거래일 후 예측을 생성하고
+          차트에 점선으로 이어 붙입니다.
+        </div>
+      )}
+    </div>
+  );
+}
+
+function fmtPct(v: number | null): string {
+  if (v == null) return "-";
+  return `${(v * 100).toFixed(0)}%`;
+}
+
+function fmtSignedPct(v: number | null): string {
+  if (v == null) return "-";
+  const pct = v * 100;
+  const sign = pct >= 0 ? "+" : "";
+  return `${sign}${pct.toFixed(2)}%`;
+}

web/components/SearchBox.tsx

@@ -0,0 +1,93 @@
+"use client";
+
+import Link from "next/link";
+import { useEffect, useState } from "react";
+import { api, type Symbol } from "../lib/api";
+
+export function SearchBox({ autoFocus = false }: { autoFocus?: boolean }) {
+  const [q, setQ] = useState("");
+  const [items, setItems] = useState<Symbol[]>([]);
+  const [loading, setLoading] = useState(false);
+  const [err, setErr] = useState<string | null>(null);
+  const [seedOnly, setSeedOnly] = useState(false);
+
+  useEffect(() => {
+    const term = q.trim();
+    if (!term) {
+      setItems([]);
+      setErr(null);
+      return;
+    }
+    setLoading(true);
+    setErr(null);
+    const handle = setTimeout(async () => {
+      try {
+        const r = await api.search(term, seedOnly, 15);
+        setItems(r.items);
+      } catch (e) {
+        setErr(e instanceof Error ? e.message : String(e));
+        setItems([]);
+      } finally {
+        setLoading(false);
+      }
+    }, 200);
+    return () => clearTimeout(handle);
+  }, [q, seedOnly]);
+
+  return (
+    <div className="w-full">
+      <div className="flex items-center gap-3">
+        <input
+          autoFocus={autoFocus}
+          value={q}
+          onChange={(e) => setQ(e.target.value)}
+          placeholder="종목명 또는 코드 (예: 삼성, 005930)"
+          className="w-full rounded-md border border-zinc-700 bg-zinc-900 px-4 py-3 text-base outline-none focus:border-zinc-500"
+        />
+        <label className="flex shrink-0 items-center gap-2 text-xs text-zinc-400">
+          <input
+            type="checkbox"
+            checked={seedOnly}
+            onChange={(e) => setSeedOnly(e.target.checked)}
+          />
+          학습대상 10종목만
+        </label>
+      </div>
+
+      <div className="mt-2 min-h-[1.5rem] text-xs text-zinc-500">
+        {loading && "검색중…"}
+        {err && <span className="text-red-400">에러: {err}</span>}
+        {!loading && !err && q && items.length === 0 && "검색 결과 없음"}
+      </div>
+
+      {items.length > 0 && (
+        <ul className="mt-2 divide-y divide-zinc-800 rounded-md border border-zinc-800 bg-zinc-900/40">
+          {items.map((it) => (
+            <li key={it.code}>
+              <Link
+                href={`/${it.code}`}
+                className="flex items-center justify-between px-4 py-3 text-sm hover:bg-zinc-800/70"
+              >
+                <div>
+                  <div className="font-medium text-zinc-100">
+                    {it.name}
+                    {it.is_seed && (
+                      <span className="ml-2 rounded-full bg-emerald-900/60 px-2 py-0.5 text-[10px] font-semibold text-emerald-300">
+                        SEED
+                      </span>
+                    )}
+                  </div>
+                  <div className="text-xs text-zinc-500">
+                    {it.code} · {it.market}
+                    {it.sector ? ` · ${it.sector}` : ""}
+                  </div>
+                </div>
+                <span className="text-zinc-500">→</span>
+              </Link>
+            </li>
+          ))}
+        </ul>
+      )}
+    </div>
+  );
+}

web/components/StockChart.tsx

@@ -0,0 +1,167 @@
+"use client";
+
+import { useEffect, useRef } from "react";
+import {
+  createChart,
+  type CandlestickData,
+  type IChartApi,
+  type ISeriesApi,
+  type LineData,
+  type UTCTimestamp,
+} from "lightweight-charts";
+import type { ChartPayload, LatestPredictionResponse } from "../lib/api";
+
+type Props = {
+  chart: ChartPayload;
+  prediction?: LatestPredictionResponse | null;
+};
+
+function dateToUtcTs(d: string): UTCTimestamp {
+  // 'YYYY-MM-DD' → UTC midnight epoch seconds
+  return (Date.UTC(
+    Number(d.slice(0, 4)),
+    Number(d.slice(5, 7)) - 1,
+    Number(d.slice(8, 10)),
+  ) / 1000) as UTCTimestamp;
+}
+
+export function StockChart({ chart, prediction }: Props) {
+  const containerRef = useRef<HTMLDivElement | null>(null);
+  const chartRef = useRef<IChartApi | null>(null);
+  const candleRef = useRef<ISeriesApi<"Candlestick"> | null>(null);
+  const predRef = useRef<ISeriesApi<"Line"> | null>(null);
+  const predLowRef = useRef<ISeriesApi<"Line"> | null>(null);
+  const predHighRef = useRef<ISeriesApi<"Line"> | null>(null);
+
+  // create chart once
+  useEffect(() => {
+    if (!containerRef.current) return;
+    const c = createChart(containerRef.current, {
+      layout: {
+        background: { color: "transparent" },
+        textColor: "#cbd5e1",
+      },
+      grid: {
+        vertLines: { color: "#1f2937" },
+        horzLines: { color: "#1f2937" },
+      },
+      rightPriceScale: { borderColor: "#374151" },
+      timeScale: { borderColor: "#374151", timeVisible: false },
+      autoSize: true,
+    });
+    const candle = c.addCandlestickSeries({
+      upColor: "#22c55e",
+      downColor: "#ef4444",
+      borderUpColor: "#22c55e",
+      borderDownColor: "#ef4444",
+      wickUpColor: "#22c55e",
+      wickDownColor: "#ef4444",
+    });
+    chartRef.current = c;
+    candleRef.current = candle;
+    return () => {
+      c.remove();
+      chartRef.current = null;
+      candleRef.current = null;
+      predRef.current = null;
+      predLowRef.current = null;
+      predHighRef.current = null;
+    };
+  }, []);
+
+  // push candle data
+  useEffect(() => {
+    if (!candleRef.current) return;
+    const data: CandlestickData[] = chart.ohlcv
+      .filter((p) => p.open !== null && p.high !== null && p.low !== null && p.close !== null)
+      .map((p) => ({
+        time: dateToUtcTs(p.date),
+        open: p.open as number,
+        high: p.high as number,
+        low: p.low as number,
+        close: p.close as number,
+      }));
+    candleRef.current.setData(data);
+    chartRef.current?.timeScale().fitContent();
+  }, [chart]);
+
+  // push prediction overlay
+  useEffect(() => {
+    if (!chartRef.current) return;
+    // remove previous overlay
+    if (predRef.current) {
+      chartRef.current.removeSeries(predRef.current);
+      predRef.current = null;
+    }
+    if (predLowRef.current) {
+      chartRef.current.removeSeries(predLowRef.current);
+      predLowRef.current = null;
+    }
+    if (predHighRef.current) {
+      chartRef.current.removeSeries(predHighRef.current);
+      predHighRef.current = null;
+    }
+    if (!prediction || !prediction.found || !prediction.steps?.length) return;
+    const baseDate = prediction.base_date!;
+    const baseClose = prediction.base_close;
+    if (!baseClose) return;
+    const sorted = [...prediction.steps].sort((a, b) => a.horizon - b.horizon);
+
+    const med: LineData[] = [
+      { time: dateToUtcTs(baseDate), value: baseClose },
+      ...sorted
+        .filter((s) => s.point_close !== null)
+        .map((s) => ({ time: dateToUtcTs(s.target_date), value: s.point_close as number })),
+    ];
+    const lo: LineData[] = [
+      { time: dateToUtcTs(baseDate), value: baseClose },
+      ...sorted
+        .filter((s) => s.ci_low !== null)
+        .map((s) => ({ time: dateToUtcTs(s.target_date), value: s.ci_low as number })),
+    ];
+    const hi: LineData[] = [
+      { time: dateToUtcTs(baseDate), value: baseClose },
+      ...sorted
+        .filter((s) => s.ci_high !== null)
+        .map((s) => ({ time: dateToUtcTs(s.target_date), value: s.ci_high as number })),
+    ];
+
+    const medLine = chartRef.current.addLineSeries({
+      color: "#a78bfa",
+      lineWidth: 2,
+      lineStyle: 2, // dashed
+      priceLineVisible: false,
+      lastValueVisible: true,
+      title: "예측 median",
+    });
+    medLine.setData(med);
+    const loLine = chartRef.current.addLineSeries({
+      color: "#7c3aed",
+      lineWidth: 1,
+      lineStyle: 1,
+      priceLineVisible: false,
+      lastValueVisible: false,
+      title: "q10",
+    });
+    loLine.setData(lo);
+    const hiLine = chartRef.current.addLineSeries({
+      color: "#7c3aed",
+      lineWidth: 1,
+      lineStyle: 1,
+      priceLineVisible: false,
+      lastValueVisible: false,
+      title: "q90",
+    });
+    hiLine.setData(hi);
+    predRef.current = medLine;
+    predLowRef.current = loLine;
+    predHighRef.current = hiLine;
+    chartRef.current.timeScale().fitContent();
+  }, [prediction]);
+
+  return (
+    <div className="h-[460px] w-full rounded-md border border-zinc-800 bg-zinc-900/30 p-2">
+      <div ref={containerRef} className="h-full w-full" />
+    </div>
+  );
+}

web/lib/api.ts

@@ -0,0 +1,178 @@
+// Backend API client.
+// NEXT_PUBLIC_API_BASE 는 docker-compose 에서 http://localhost:8000 으로 주입됨.
+
+const RAW_BASE = process.env.NEXT_PUBLIC_API_BASE ?? "http://localhost:8000";
+export const API_BASE = RAW_BASE.replace(/\/$/, "");
+
+export type Symbol = {
+  code: string;
+  name: string;
+  market: string;
+  sector: string | null;
+  is_seed: boolean;
+};
+
+export type SymbolSearch = {
+  q: string;
+  count: number;
+  items: Symbol[];
+};
+
+export type OhlcvPoint = {
+  date: string;
+  open: number | null;
+  high: number | null;
+  low: number | null;
+  close: number | null;
+  volume: number | null;
+};
+
+export type SentimentPoint = {
+  date: string;
+  n_articles: number;
+  mean_score: number | null;
+  weighted_score: number | null;
+};
+
+export type TradingValuePoint = {
+  date: string;
+  foreign_net: number | null;
+  institution_net: number | null;
+  individual_net: number | null;
+};
+
+export type ChartPayload = {
+  code: string;
+  name: string;
+  market: string;
+  range: { from: string; to: string };
+  ohlcv: OhlcvPoint[];
+  sentiment: SentimentPoint[];
+  trading_value: TradingValuePoint[];
+};
+
+export type PredictionStep = {
+  horizon: number;
+  target_idx?: number;
+  point_close: number;
+  ci_low: number;
+  ci_high: number;
+  prob_up: number;
+  prob_flat: number;
+  prob_down: number;
+  direction: "up" | "flat" | "down";
+  expected_return: number;
+  target_date?: string;
+};
+
+export type PredictResponse = {
+  code: string;
+  base_date: string;
+  base_close: number;
+  sources_used: string[];
+  steps: PredictionStep[];
+  saved_prediction_ids: number[];
+  user_triggered: boolean;
+};
+
+export type LatestPredictionStep = {
+  predicted_at: string | null;
+  target_date: string;
+  horizon: number;
+  direction: "up" | "flat" | "down" | string;
+  prob_up: number | null;
+  prob_flat: number | null;
+  prob_down: number | null;
+  expected_return: number | null;
+  point_close: number | null;
+  ci_low: number | null;
+  ci_high: number | null;
+  user_triggered: boolean;
+  features_snapshot: unknown;
+};
+
+export type LatestPredictionResponse = {
+  code: string;
+  name?: string;
+  found: boolean;
+  base_date?: string;
+  base_close?: number | null;
+  steps: LatestPredictionStep[];
+};
+
+export type MetricsRow = {
+  model: string;
+  horizon: number;
+  n: number;
+  hit_rate: number | null;
+  mae: number | null;
+};
+
+export type MetricsResponse = {
+  code?: string;
+  name?: string;
+  window_days: number;
+  range: { from: string; to: string };
+  by_model_horizon: MetricsRow[];
+};
+
+export type NewsItem = {
+  source: string;
+  published_at: string | null;
+  title: string;
+  url: string;
+  sentiment_score: number | null;
+  sentiment_label: string | null;
+};
+
+export type NewsResponse = {
+  code: string;
+  name: string;
+  count: number;
+  items: NewsItem[];
+};
+
+async function getJson<T>(path: string, init?: RequestInit): Promise<T> {
+  const res = await fetch(`${API_BASE}${path}`, {
+    ...init,
+    headers: {
+      Accept: "application/json",
+      ...(init?.headers ?? {}),
+    },
+    cache: "no-store",
+  });
+  if (!res.ok) {
+    const text = await res.text().catch(() => "");
+    throw new Error(`API ${path} → ${res.status} ${text || res.statusText}`);
+  }
+  return (await res.json()) as T;
+}
+
+export const api = {
+  search: (q: string, seedOnly = false, limit = 20) =>
+    getJson<SymbolSearch>(
+      `/api/symbols/search?q=${encodeURIComponent(q)}&limit=${limit}&seed_only=${seedOnly}`,
+    ),
+  getSymbol: (code: string) => getJson<Symbol>(`/api/symbols/${encodeURIComponent(code)}`),
+  getChart: (code: string, days = 180) =>
+    getJson<ChartPayload>(`/api/chart/${encodeURIComponent(code)}?days=${days}`),
+  predict: (code: string, horizons = "1,3,5") =>
+    getJson<PredictResponse>(
+      `/api/predict/${encodeURIComponent(code)}?horizons=${encodeURIComponent(horizons)}`,
+      { method: "POST" },
+    ),
+  latestPrediction: (code: string) =>
+    getJson<LatestPredictionResponse>(`/api/predict/${encodeURIComponent(code)}/latest`),
+  metrics: (code: string, windowDays = 30) =>
+    getJson<MetricsResponse>(
+      `/api/metrics/${encodeURIComponent(code)}?window_days=${windowDays}`,
+    ),
+  overallMetrics: (windowDays = 30) =>
+    getJson<MetricsResponse>(`/api/metrics?window_days=${windowDays}`),
+  news: (code: string, limit = 20, source?: string) =>
+    getJson<NewsResponse>(
+      `/api/news/${encodeURIComponent(code)}?limit=${limit}${
+        source ? `&source=${encodeURIComponent(source)}` : ""
+      }`,
+    ),
+};

web/package.json

@@ -6,7 +6,9 @@
     "dev": "next dev -p 3000 -H 0.0.0.0",
     "build": "next build",
     "start": "next start -p 3000 -H 0.0.0.0",
-    "lint": "next lint"
+    "lint": "next lint",
+    "typecheck": "tsc --noEmit",
+    "check": "npm run typecheck && npm run lint"
   },
   "dependencies": {
     "next": "14.2.3",
@@ -21,6 +23,8 @@
     "typescript": "5.4.5",
     "tailwindcss": "3.4.4",
     "postcss": "8.4.38",
-    "autoprefixer": "10.4.19"
+    "autoprefixer": "10.4.19",
+    "eslint": "8.57.0",
+    "eslint-config-next": "14.2.3"
   }
 }

web/tsconfig.json

@@ -13,6 +13,9 @@
     "isolatedModules": true,
     "jsx": "preserve",
     "incremental": true,
+    "forceConsistentCasingInFileNames": true,
+    "noFallthroughCasesInSwitch": true,
+    "noImplicitOverride": true,
     "plugins": [{ "name": "next" }],
     "paths": { "@/*": ["./*"] }
   },